Motion edges and regions guide image segmentation by colour.

Image segmentation is an important early stage in visual processing in which the visual system groups together parts of the image that belong together, prior to or in conjunction with object recognition. Two principal processes may be involved in image segmentation: an edge-based process that uses feature contrasts to mark boundaries of coherent regions, and a region-based process that groups similar features over a larger scale. Earlier, we have shown that motion and colour interact strongly in image segmentation by the human visual system. Here we explore the nature of this interaction in terms of edge- and region-based processes. We measure performance on a region-based colour segmentation task in the presence of distinct types of motion information, in the form of edges and regions which in themselves do not reveal the location of the colour target. The results show that both motion edges and regions may guide the integrative process required for this colour segmentation task. Motion edges appear to act by delimiting areas over which to integrate colour information, whereas motion similarities define primitive surfaces within which colour grouping and segmentation processes are deployed.     

Creating abstract topographic representations: Implications for coding,learning and reasoning

Topographic maps are a fundamental and ubiquitous feature of the sensory and motor regions of the brain. There is less evidence for the existence of conventional topographic maps in associational areas of the brain such as the prefrontal cortex and parietal cortex. The existence of topographically arranged anatomical projections is far more widespread and occurs in associational regions of the brain as well as sensory and motor regions: this points to a more widespread existence of topographically organised maps within associational cortex than currently recognised. Indeed, there is increasing evidence that abstract topographic representations may also occur in these regions. For example, a topographic mnemonic map of visual space has been described in the dorsolateral prefrontal cortex and topographically arranged visuospatial attentional signals have been described in parietal association cortex. This article explores how abstract representations might be extracted from sensory topographic representations and subsequently code abstract information. Finally a simple model is presented that shows how abstract topographic representations could be integrated with other information within the brain to solve problems or form abstract associations. The model uses correlative firing to detect associations between different types of stimuli. It is flexible because it can produce correlations between information represented in a topographic or non-topographic coordinate system. It is proposed that a similar process could be used in high-level cognitive operations such as learning and reasoning.     

Spatial changes in poly(A) concentrations during early embryogenesis in Xenopus laevis: analysis by in situ hybridization

The dorsal-ventral axis of the Xenopus embryo is approximately determined by the point of sperm entry, which occurs randomly about the animal-vegetal axis of the egg. Two cytoplasmic components were studied whose spatial arrangements and local concentration patterns appear to reflect the forming dorsal-ventral axis. First, three-dimensional maps depicting the rearrangements of different size classes of yolk platelets were reconstructed from serial sections. These maps provide information on the time when movements occur and the intracellular directions and distances components can travel within the early embryo. Second, local poly(A) concentrations were observed using a [3H]poly(U) in situ hybridization probe. Poly(A) concentrations were observed to change reproducibly from fertilization to late cleavage. Two cytoplasmic areas were found to undergo major, stage-specific increases in poly(A) concentration.     

Iconic memory

Investigations of the processing of brief visual displays, and the explanation of such processing in terms of iconic memory, are reviewed. It is concluded that the concept of a pre-categorical sensory memory for visual material remains tenable. The ability to report material from brief visual displays is seen as depending upon parallel (and perhaps unlimited) transfer from iconic memory to a post-categorical memory mode, followed by a limited (and perhaps serial) transfer to an output stage. Decisions about, or responses to, items can only be made when they are in the output stage. Because transfer out of the post-categorical mode can be performed on the basis of pre-categorical stimulus features, pre-categorical information about items in the post-categorical mode must be accessible. One way in which this would be possible is if the transfer of an item into the post-categorical mode takes the form of the creation, to represent the item, of a temporary file of information including both pre-categorical and post-categorical features of the item. Any such feature can be used as the basis for selecting the item for transfer from the post-categorical mode to the output stage, for subsequent decision or report.     

AMASS: algorithm for MSI analysis by semi-supervised segmentation

Mass Spectrometric Imaging (MSI) is a molecular imaging technique that allows the generation of 2D ion density maps for a large complement of the active molecules present in cells and sectioned tissues. Automatic segmentation of such maps according to patterns of co-expression of individual molecules can be used for discovery of novel molecular signatures (molecules that are specifically expressed in particular spatial regions). However, current segmentation techniques are biased toward the discovery of higher abundance molecules and large segments; they allow limited opportunity for user interaction, and validation is usually performed by similarity to known anatomical features. We describe here a novel method, AMASS (Algorithm for MSI Analysis by Semi-supervised Segmentation). AMASS relies on the discriminating power of a molecular signal instead of its intensity as a key feature, uses an internal consistency measure for validation, and allows significant user interaction and supervision as options. An automated segmentation of entire leech embryo data images resulted in segmentation domains congruent with many known organs, including heart, CNS ganglia, nephridia, nephridiopores, and lateral and ventral regions, each with a distinct molecular signature. Likewise, segmentation of a rat brain MSI slice data set yielded known brain features and provided interesting examples of co-expression between distinct brain regions. AMASS represents a new approach for the discovery of peptide masses with distinct spatial features of expression. Software source code and installation and usage guide are available at http://bix.ucsd.edu/AMASS/ .     

Contact-Free Palm-Vein Recognition Based on Local Invariant Features

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.     

A Proto-Architecture for Innate Directionally Selective Visual Maps

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.     

Heuristic Bayesian Segmentation for Discovery of Coexpressed Genes within Genomic Regions

Segmentation aims to separate homogeneous areas from the sequential data, and plays a central role in data mining. It has applications ranging from finance to molecular biology, where bioinformatics tasks such as genome data analysis are active application fields. In this paper, we present a novel application of segmentation in locating genomic regions with coexpressed genes. We aim at automated discovery of such regions without requirement for user-given parameters. In order to perform the segmentation within a reasonable time, we use heuristics. Most of the heuristic segmentation algorithms require some decision on the number of segments. This is usually accomplished by using asymptotic model selection methods like the Bayesian information criterion. Such methods are based on some simplification, which can limit their usage. In this paper, we propose a Bayesian model selection to choose the most proper result from heuristic segmentation. Our Bayesian model presents a simple prior for the segmentation solutions with various segment numbers and a modified Dirichlet prior for modeling multinomial data. We show with various artificial data sets in our benchmark system that our model selection criterion has the best overall performance. The application of our method in yeast cell-cycle gene expression data reveals potential active and passive regions of the genome.     

Eff-UNet++: A novel architecture for plant leaf segmentation and counting

Leaf segmentation learns more about leaf-level traits such as leaf area, count, stress, and development phases. In plant phenotyping, segmentation and counting of plant organs like leaves are a major challenge due to considerable overlap between leaves and varying environmental conditions, including brightness variation and shadow, blur due to wind. Further, the plant's inherent challenges, such as the leaf texture, genotype, size, shape, and density variation of leaves, make the leaf segmentation task more complex. To meet these challenges, the present work proposes a novel method for leaf segmentation and counting employing Eff-Unet++, an encoder-decoder-based architecture. This architecture uses EfficientNet-B4 as an encoder for accurate feature extraction. The redesigned skip connections and residual block in the decoder utilize encoder output and help to address the information degradation problem. In addition, the redesigned skip connections reduce the computational complexity. The lateral output layer is introduced to aggregate the low-level to high-level features from the decoder, which improves segmentation performance. The proposed method validates its performance on three datasets: KOMATSUNA dataset, Multi-Modality Plant Imagery Dataset (MSU-PID), and Computer Vision for Plant Phenotyping dataset (CVPPP). The proposed approach outperforms the existing state-of-the-art methods UNet, UNet++, Residual-UNet, InceptionResv2-UNet, and DeeplabV3 leaf segmentation results achieve best dice (BestDice): 83.44, 71.17, 78.27 and Foreground-Background Dice (FgBgDice): 97.48, 91.35, 96.38 on KOMATSUNA, MSU-PID, and CVPPP dataset respectively. In addition, for leaf counting the results are difference in count (DiffFG): 0.11, 0.03, 0.12 and Absolute Difference in count (AbsDiffFG): 0.21, 0.38, 1.27 on KOMATSUNA, MSU-PID, and CVPPP dataset respectively.     

Models for preattentive texture discrimination: Fourier analysis and local feature processing in a unified framework

Spatial frequency analysis and local feature analysis may be considered to be examples of a class of models for texture discrimination. In this theoretical framework, texture discrimination relies on differences in the distribution of responses generated in linear receptive fields placed randomly on the texture. If the set of receptive fields is taken to be a collection of gratings, spatial-frequency analysis is recovered. If the set of receptive-fields is taken to be a collection of local feature templates, a corresponding local-feature model is recovered. In order to test such models, it is necessary to construct distinct texture pairs that elicit similar distributions of responses for all of the postulated receptive field profiles: the model prediction is that such textures are not discriminable. A method is provided for construction of such textures which test generic models within this framework. This framework includes not only strict Fourier analysis, but also models which postulate a collection of arbitrarily-shaped local feature detectors, and models which postulate both Fourier analysis and local feature detection.     

Frequency-domain analysis of biomolecular sequences

MOTIVATION: Frequency-domain analysis of biomolecular sequences is hindered by their representation as strings of characters. If numerical values are assigned to each of these characters, then the resulting numerical sequences are readily amenable to digital signal processing. RESULTS: We introduce new computational and visual tools for biomolecular sequences analysis. In particular, we provide an optimization procedure improving upon traditional Fourier analysis performance in distinguishing coding from noncoding regions in DNA sequences. We also show that the phase of a properly defined Fourier transform is a powerful predictor of the reading frame of protein coding regions. Resulting color maps help in visually identifying not only the existence of protein coding areas for both DNA strands, but also the coding direction and the reading frame for each of the exons. Furthermore, we demonstrate that color spectrograms can visually provide, in the form of local 'texture', significant information about biomolecular sequences, thus facilitating understanding of local nature, structure and function.     

Genes and the physics of the DNA double-helix

The processing of the genetic information stored in the double-helical DNA implies the separation of the two strands, the physics of which is described by the helix-coil transition model. Is there a relationship between genetic maps and DNA physical stability maps that plot the sequence-specific propensity for the thermal disruption of the double-helix? Here, with appropriate methodological formulations, such maps are derived for a large set of sequences, including complete genomes. The superposition of the two maps leads to a contrasted picture with correlations ranging between two extremes: from almost perfect (with the genes precisely delineated as stable regions) to more or less complete unrelatedness. The simplest explanation for the results is that the observed striking correlations correspond to the relics of a primeval organisation of the genetic message, with the physics of DNA playing a role in the delimitation of coding regions. In order to trace the evolutionary fate of this signal further, a detailed study of the yeast complete genome is performed. In this study, the superposition of the genetic and physical stability maps is examined in the light of information concerning gene duplication. On the basis of this analysis it is concluded that the 'signature' associated with the supposed archaic signal is in the process of being erased, most probably because the underlying feature is no longer under selective pressure. There are many evolutionary implications for the results presented and for their proposed interpretations, notably concerning models of mutational dynamics in relation to erasure processes.     

Graph ranking based butterfly segmentation in ecological images

An accurate cultural insect detection and recognition relies mainly on a proper automatic segmentation. This paper deals with butterfly segmentation in ecological images characterized by several artifacts like the complexity of environmental decors and cluttered backgrounds. The distractors contained in the rich ecological environment and the huge difference between butterfly species complicate severely the segmentation and make it a challenging task. As butterflies appears to be well contrasted from their surrounding, we suggest to explore the saliency property to delineate accurately the butterfly boundaries. In this vein, we perform a graph ranking process with high level guidance according to foreground and background cues to improve the quality of segmentation. The ranking accuracy is improved through a weighting scheme that combines accurately color, texture and spatial information. The contribution of each used feature is controlled according to its relevance in highlighting butterfly regions. After that, we initialize foreground seeds from most salient pixels and background seeds from less salient pixels as an input for a Graph-cut algorithm to extract the butterfly from the background. Comparative evaluation has shown that our segmentation scheme outperforms some existing segmentation methods that provide high segmentation scores.     

Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale

The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.     

Robust sound classification through the representation of similarity using response fields derived from stimuli during early experience

Models of auditory processing, particularly of speech, face many difficulties. Included in these are variability among speakers, variability in speech rate, and robustness to moderate distortions such as time compression. We constructed a system based on ensembles of feature detectors derived from fragments of an onset-sensitive sound representation. This method is based on the idea of ‘spectro-temporal response fields’ and uses convolution to measure the degree of similarity through time between the feature detectors and the stimulus. The output from the ensemble was used to derive segmentation cues and patterns of response, which were used to train an artificial neural network (ANN) classifier. This allowed us to estimate a lower bound for the mutual information between the class of the input and the class of the output. Our results suggest that there is significant information in the output of our system, and that this is robust with respect to the exact choice of feature set, time compression in the stimulus, and speaker variation. In addition, the robustness to time compression in the stimulus has features in common with human psychophysics. Similar experiments using feature detectors derived from fragments of non-speech sounds performed less well. This result is interesting in the light of results showing aberrant cortical development in animals exposed to impoverished auditory environments during the developmental phase.     

The coordinated mapping of visual space and response features in visual cortex

Whether general principles can explain the layouts of cortical maps remains unresolved. In primary visual cortex of ferret, the relationships between the maps of visual space and response features are predicted by a "dimension-reduction" model. The representation of visual space is anisotropic, with the elevation and azimuth axes having different magnification. This anisotropy is reflected in the orientation, ocular dominance, and spatial frequency domains, which are elongated such that their directions of rapid change, or high-gradient axes, are orthogonal to the high-gradient axis of the visual map. The feature maps are also strongly interdependent-their high-gradient regions avoid one another and intersect orthogonally where essential, so that overlap is minimized. Our results demonstrate a clear influence of the visual map on each feature map. In turn, the local representation of visual space is smooth, as predicted when many features are mapped within a cortical area.     

Anatomical Feature Segmentation of Femur Point Cloud Based on Medical Semantics

Feature segmentation is an essential phase for geometric modeling and shape processing in anatomical study of human skeleton and clinical digital treatment of orthopedics. Due to various degrees of freedom of bone surface, the existing segmentation algorithms can hardly meet specific medical need. To address this, a novel segmentation methodology for anatomical features of femur model based on medical semantics is put forward. First, anatomical reference objects (ARO) are created to represent typical characteristics of femur anatomy by 3D point fitting in combination with medical priori knowledge. Then, local point clouds between adjacent anatomies are selected according to the AROs to extract boundary feature point (BFP)s. Finally, the complete model of femur is divided into anatomical regions by executing the enhanced watershed algorithm guided with BFPs. Experimental results show that the proposed method has the advantages of automatic segmentation of femoral head, neck and other complex areas, and the segmentation results have better medical semantics. In addition, the slight modification of segmentation results can be achieved by adjusting a few threshold parameter values, which improves the convenience of modification for ordinary users.