Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes.     

Classifying and ranking audio clips to support bird species richness surveys

Advances in programmable field acoustic sensors provide immense data for bird species study. Manually searching for bird species present in these acoustic data is time-consuming. Although automated techniques have been used for species recognition in many studies, currently these techniques are prone to error due to the complexity of natural acoustics.In this paper we propose a smart sampling approach to help identify the maximum number of bird species while listening to the minimum amount of acoustic data. This approach samples audio clips in a manner that can direct bird species surveys more efficiently. First, a classifier is built to remove audio clips that are unlikely to contain birds; second, the remaining audio clips are ranked by a proxy for the number of species. This technique enables a more efficient determination of species richness.The experimental results show that the use of a classifier enables to remove redundant acoustic data and make our approach resilient to various weather conditions. By ranking audio clips classified as “Birds”, our method outperforms the currently best published strategy for finding bird species after 30 one-minute audio clip samples. Particularly after 60 samples, our method achieves 10 percentage points more species. Despite our focus on bird species, the proposed sampling approach is applicable to the search of other vocal species.     

Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.     

Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning

Over the last years, researchers have addressed the automatic classification of calling bird species. This is important for achieving more exhaustive environmental monitoring and for managing natural resources. Vocalisations help to identify new species, their natural history and macro-systematic relations, while computer systems allow the bird recognition process to be sped up and improved. In this study, an approach that uses state-of-the-art features designed for speech and speaker state recognition is presented. A method for voice activity detection was employed previous to feature extraction. Our analysis includes several classification techniques (multilayer perceptrons, support vector machines and random forest) and compares their performance using different configurations to define the best classification method. The experimental results were validated in a cross-validation scheme, using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America). The results show that a high classification rate, close to 90%, is obtained for this family in this Furnariidae group using the proposed features and classifiers.     

弯曲姿态蛾类幼虫的自动识别方法研究

【目的】为害态幼虫现场识别时, 幼虫常出现姿态弯曲情况, 使提取的特征向量失真, 影响幼虫的匹配识别结果。本文提出了一种基于扇形变换的姿态不变胡氏矩特征向量提取方法, 提取的病害幼虫特征向量具有平移、 比例、 旋转和姿态不变性, 可以实现粗短弯曲姿态幼虫的自动识别。【方法】首先在幼虫图像细化的基础上采用最优一致逼近法确定了幼虫的弯曲区域和非弯曲区域。然后, 幼虫的弯曲区域采用扇形变换实现校正变直, 非弯曲区域经旋转和平移与扇形变换后的区域拼接组成完整虫体; 采用八邻域均值法填充变换后虫体区域中的空白点, 实现幼虫像的弯曲自动校正; 在此基础上提取胡氏不变矩具有姿态不变性, 采用最小距离分类器实现了多姿态幼虫的自动识别。最后, 以多种弯曲姿态的斜纹夜蛾Prodenia litura、 棉铃虫Heliocoverpa armigera、 甜菜夜蛾Spodoptera exigua、 玉米螟Ostrinia nubilalis等病害蛾类幼虫为识别对象进行了识别验证。【结果】对于24种不同姿态的幼虫图像, 在80%的识别阈值条件下, 基于经典胡氏不变矩的幼虫识别率为25%, 基于姿态不变胡氏矩的识别率为100%。【结论】实验结果表明该方法对多种弯曲姿态的粗短幼虫具有较高的识别率。     

基于发育网络识别模型的大熊猫面部识别

个体识别是动物行为学与生态学研究工作的基础,也是制定珍稀野生动物保护政策的重要依据。为了丰富大熊猫个体识别和种群数量调查的方法,我们于2017年7月分别在四川省雅安市碧峰峡大熊猫基地和四川省汶川县耿达镇的中华大熊猫苑共计拍摄18只大熊猫个体,每只大熊猫拍摄6~13张高质量面部照片（共计131张）,利用发育网络（Developmental Network）建立大熊猫面部识别模型。利用此模型对存在部分背景的大熊猫面部照片进行识别检测,得到的个体识别率为79.41%,对完全去除背景的大熊猫面部照片进行识别检测,得到的个体识别率为58.82%。研究表明,发育网络具有足够的大熊猫个体识别能力,不同背景比例的照片对大熊猫个体识别的实际结果具有较大的影响。随着发育网络识别模型的发展,我们建议更多的野生动物保护研究者结合这一技术深入地开展珍稀野生动物（如大熊猫）个体识别研究,逐步提高识别准确度,并应用到关键区域大规模的动物调查中。     

Palm-Vein Classification Based on Principal Orientation Features

Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm–vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm–vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm–vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm–vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database.     

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition.     

“ELISA” as an aid in the identification of fish and molluscan prey of birds in marine ecosystems

A serological technique known as ELISA (enzyme-linked immunosorbent assay) was used in an attempt to aid the identification of visually unidentifiable seabird stomach contents. A series of seabird-prey muscle-protein antisera was established. When these antisera were tested against pieces of digested and undigested prey species, the ELISA technique detected the prey from both digested and undigested samples. This method also enabled rapid quantitative analysis of the samples.     

Investigating brown rat <Emphasis Type="Italic">Rattus norvegicus</Emphasis> egg predation using experimental nests and camera traps

Introduced rat species have been implicated in the decline and local extirpation of numerous seabird species from islands across the globe, leading to widespread eradications as a conservation tool. However, little conclusive evidence has been established to determine the direct mechanisms in which rat and seabird species interact. This study aimed to quantify rates of egg predation by brown rats Rattus norvegicus using automated trail cameras at seabird nests baited with domestic quail and hen eggs to represent different-sized seabird eggs. The trail cameras were in situ for a total of 915 days during June and July 2011. Evidence for rats visiting the experimental nests was only observed at one location, where 19 visits were recorded, and no evidence of rat predation on hen or quail eggs was observed. Low levels of rat activity were observed during the study; therefore, it is not possible to conclude that rats never predate seabird eggs as predation may be more likely when rat densities are higher and pressure on food resources greater. This study does however highlight that where rats occur at low densities, predation of eggs is unlikely. Measures aimed at maintaining low abundance of rats in and around vulnerable seabird colonies may therefore be useful where complete eradication is not feasible, although potential for predation of chicks should also be considered.     

Manta Matcher: automated photographic identification of manta rays using keypoint features

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern‐matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale‐Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern‐matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life‐history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world.     

A Time-Critical Adaptive Approach for Visualizing Natural Scenes on Different Devices

To automatically adapt to various hardware and software environments on different devices, this paper presents a time-critical adaptive approach for visualizing natural scenes. In this method, a simplified expression of a tree model is used for different devices. The best rendering scheme is intelligently selected to generate a particular scene by estimating the rendering time of trees based on their visual importance. Therefore, this approach can ensure the reality of natural scenes while maintaining a constant frame rate for their interactive display. To verify its effectiveness and flexibility, this method is applied in different devices, such as a desktop computer, laptop, iPad and smart phone. Applications show that the method proposed in this paper can not only adapt to devices with different computing abilities and system resources very well but can also achieve rather good visual realism and a constant frame rate for natural scenes.     

A Novel Method of Automatic Plant Species Identification Using Sparse Representation of Leaf Tooth Features

Automatic species identification has many advantages over traditional species identification. Currently, most plant automatic identification methods focus on the features of leaf shape, venation and texture, which are promising for the identification of some plant species. However, leaf tooth, a feature commonly used in traditional species identification, is ignored. In this paper, a novel automatic species identification method using sparse representation of leaf tooth features is proposed. In this method, image corners are detected first, and the abnormal image corner is removed by the PauTa criteria. Next, the top and bottom leaf tooth edges are discriminated to effectively correspond to the extracted image corners; then, four leaf tooth features (Leaf-num, Leaf-rate, Leaf-sharpness and Leaf-obliqueness) are extracted and concatenated into a feature vector. Finally, a sparse representation-based classifier is used to identify a plant species sample. Tests on a real-world leaf image dataset show that our proposed method is feasible for species identification.     

Fluorescence-assisted image analysis of freshwater microalgae

We exploit a property of microalgae-that of their ability to autofluoresce when exposed to epifluorescence illumination-to tackle the problem of detecting and analysing microalgae in sediment samples containing complex scenes. We have added fluorescence excitation to the hardware portion of our microalgae image processing system. We quantitatively measured 120 characteristics of each object detected through fluorescence excitation, and used an optimized subset of these characteristics for later automated analysis and species classification. All specimens used for training and testing our system came from natural populations found in Lake Biwa, Japan. Without the use of fluorescence excitation, automated analysis of images containing algae specimens in sediment is near impossible. We also used fluorescence imaging to target microalgae in water samples containing large numbers of obtrusive nontargeted objects, which would otherwise slow processing speed and decrease species analysis and classification accuracy. Object drift problems associated with the necessity to use both a fluorescence and greyscale image of each microscope scene were solved using techniques such as template matching and a novel form of automated seeded region growing (SRG). Our system proved to be not only user-friendly, but also highly accurate in classifying two major genera of microalgae found in Lake Biwa-the cyanobacteria Anabaena spp. and Microcystis spp. Classification accuracy was measured to be over 97%.     

Performance of optimized McRAPD in identification of 9 yeast species frequently isolated from patient samples: potential for automation

Background  

Rapid, easy, economical and accurate species identification of yeasts isolated from clinical samples remains an important challenge for routine microbiological laboratories, because susceptibility to antifungal agents, probability to develop resistance and ability to cause disease vary in different species. To overcome the drawbacks of the currently available techniques we have recently proposed an innovative approach to yeast species identification based on RAPD genotyping and termed McRAPD (Melting curve of RAPD). Here we have evaluated its performance on a broader spectrum of clinically relevant yeast species and also examined the potential of automated and semi-automated interpretation of McRAPD data for yeast species identification.     

Genetic diversity of genus Vespa including an invaded species of V. velutina (Hymenoptera: Vespidae) in Korea inferred from DNA barcoding data

With about 5000 known species, the Vespidae is a large family belongs to order Hymenoptera. The genus Vespa with 22 species is one of the four genera of the subfamily Vespinae. In Korea, 10 species and subspecies are recognized. Because of their social behavior, their treat to human health and their impact in apiculture, the reliable and sometimes automated identification of these insects to species level are important. To test the efficacy of DNA barcoding method for identification of species of the genus Vespa in Korea, 30 samples of eight Korean species of genus Vespa were collected and mitochondrial DNAs of 658 bp fragment cytochrome oxidase subunit 1 (CO1) region were sequenced. A Bayesian Inference based on COI gene of the Korean Vespa species was constructed. The phylogenetic tree shoed that identification of all specimens is possible based on COI gene and we found strong relation between the sequences of the collected species from different localities in South Korea which clustered together with 100% support with sequences of the same species in GenBank. The results demonstrate that DNA barcoding is a useful technique for rapid and accurate species recognition in Korean Vespa species. The DNA barcode part of COI for V. binghami is provided for the first time that can help for identification of this species through DNA barcoding. Also, the genetic diversity among Korean Vespa velutina was zero suggests that the invasion might have occurred in a single event with small number of founders.     

Deep learning-based segmentation for disease identification

Plant diseases have recently increased and exacerbated due to several factors such as climate change, chemicals’ misuse and pollution. They represent a severe threat for both economy and global food security. Recently, several researches have been proposed for plant disease identification through modern image-based recognition systems based on deep learning. However, several challenges still require further investigation. One is related to the high variety of leaf diseases/ species along with constraints related to the collection and annotation of real-world datasets. Other challenges are related to the study of leaf disease in uncontrolled environment. Compared to major existing researches, we propose in this article a new perspective to handle the problem with two main differences: First, while most approach aims to identify simultaneously a pair of species-disease, we propose to identify diseases independently of leaf species. This helps to recognize new species holding diseases that were previously learnt. Moreover, instead of using the global leaf image, we directly predict disease on the basis of the local disease symptom features. We believe that this may decrease the bias related to common context and/or background and enables to build a more generalised model for disease classification. In particular, we propose an hybrid system that combines strengths of deep learning-based semantic segmentation with classification capabilities to respectively extract infected regions and determine their identity. For that, an extensive experimentation including a comparison of different semantic segmentation and classification CNNs has been conducted on PlantVillage dataset (leaves within homogeneous background) in order to study the extent of use of local disease symptoms features to identify diseases. Specifically, a particular enhancement of disease identification accuracy has been demonstrated in IPM and BING datasets (leaves within uncontrolled background).     

Coding of Visual Object Features and Feature Conjunctions in the Human Brain

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object''s features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process - while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200–400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.     

E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns

DNA microarrays may be used to identify microbial species present in environmental and clinical samples. However, automated tools for reliable species identification based on observed microarray hybridization patterns are lacking. We present an algorithm, E-Predict, for microarray-based species identification. E-Predict compares observed hybridization patterns with theoretical energy profiles representing different species. We demonstrate the application of the algorithm to viral detection in a set of clinical samples and discuss its relevance to other metagenomic applications.