Automatic Extraction of Closed Contours Bounding Salient Objects: New Algorithms and Evaluation Methods

The problem under consideration in this dissertation is achieving salient object segmentation of natural images by means of probabilistic contour grouping. The goal is to extract the simple closed contour bounding the salient object in a given image. The method proposed here falls in the Contour Grouping category, searching for the optimal grouping of boundary entities to form an object contour. Our first contribution is to provide both a ground truth dataset and a performance measure for empirical evaluation of salient object segmentation methods. Our Salient Object Dataset (SOD) provides ground truth boundaries of salient objects perceived by humans in natural images. We also psychophysically evaluated 5 distinct performance measures that have been used in the literature and showed that a measure based upon minimal contour mappings is most sensitive to shape irregularities and most consistent with human judgements. In fact, the Contour Mapping measure is as predictive of human judgements as human subjects are of each other. Contour grouping methods often rely on Gestalt cues locally defined on pairs of oriented features. Accurate integration of these local cues with global cues is a challenge. A second major contribution of this dissertation is a novel, effective method for combining local and global cues. A third major contribution in this dissertation is a novel method based on Principal Component Analysis for promoting diversity among contour hypotheses, leading to substantial improvements in grouping performance. To further improve the performance, a multiscale implementation of this method has been studied. A fourth contribution in this dissertation is studying the effect of the multiscale prior on the performance and analysing the method for combining the results obtained in different resolutions. Our final contribution is comparing the performance of univariate distribution models for local cues used by our method with the use of a multivariate mixture model for their joint distribution. We obtain slight improvement by the mixture models. The proposed method has been evaluated and compared with four other state-of-the-art grouping methods, showing considerably better performance on the SOD ground truth dataset

Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping

This paper presents a novel real-time method for tracking salient closed boundaries from video image sequences. This method operates on a set of straight line segments that are produced by line detection. The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one. Specifically, we define a new tracking criterion which combines a grouping cost and an area similarity constraint. The proposed criterion makes the resulting boundary tracking more robust to local minima. To achieve real-time tracking performance, we use Delaunay Triangulation to build a graph model with the detected line segments and then reduce the tracking problem to finding the optimal cycle in this graph. This is solved by our newly proposed closed boundary candidates searching algorithm called "Bidirectional Shortest Path (BDSP)". The efficiency and robustness of the proposed method are tested on real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) submission ID 103

A Framework for Symmetric Part Detection in Cluttered Scenes

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favor as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today's recognition systems are faced with cluttered scenes, and the assumption that a closed contour exists, i.e. that figure-ground segmentation has been solved, renders much of the medial axis community's work inapplicable. In this article, we review a computational framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009, 2013), that bridges the representation power of the medial axis and the need to recover and group an object's parts in a cluttered scene. Our framework is rooted in the idea that a maximally inscribed disc, the building block of a medial axis, can be modeled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.Comment: 10 pages, 8 figure

Linking Visual Cortical Development to Visual Perception

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Large Scale Power Spectrum from Peculiar Velocities Via Likelihood Analysis

The power spectrum (PS) of mass density fluctuations, independent of `biasing', is estimated from the Mark III catalog of peculiar velocities using Bayesian statistics. A parametric model is assumed for the PS, and the free parameters are determined by maximizing the probability of the model given the data. The method has been tested using detailed mock catalogs. It has been applied to generalized CDM models with and without COBE normalization. The robust result for all the models is a relatively high PS, with $P(k) \Omega^{1.2} = (4.8 \pm 1.5) \times 10^3 (Mpc/h)^3$ at $k=0.1 h/Mpc$. An extrapolation to smaller scales using the different CDM models yields $\sigma_8 \Omega^{0.6} = 0.88 \pm 0.15$. The peak is weakly constrained to the range $0.02 \leq k \leq 0.06 h/Mpc$. These results are consistent with a direct computation of the PS (Kolatt & Dekel 1996). When compared to galaxy-density surveys, the implied values for $\beta$ ($\equiv \Omega^{0.6}/b$) are of order unity to within 25%. The parameters of the COBE-normalized, flat CDM model are confined by a 90% likelihood contour of the sort $\Omega h_{50}^\mu n^\nu = 0.8 \pm 0.2$, where $\mu = 1.3$ and $\nu = 3.4, 2.0$ for models with and without tensor fluctuations respectively. For open CDM the powers are $\mu = 0.95$ and $\nu = 1.4$ (no tensor fluctuations). A $\Gamma$-shape model free of COBE normalization yields only a weak constraint: $\Gamma = 0.4 \pm 0.2$.Comment: 19 pages, 8 figures, 2 tables. Accepted for publication in The Astrophysical Journa

A Neural Model of How Horizontal and Interlaminar Connections of Visual Cortex Develop into Adult Circuits that Carry Out Perceptual Grouping and Learning

A neural model suggests how horizontal and interlaminar connections in visual cortical areas Vl and V2 develop within a laminar cortical architecture and give rise to adult visual percepts. The model suggests how mechanisms that control cortical development in the infant lead to properties of adult cortical anatomy, neurophysiology, and visual perception. The model clarifies how excitatory and inhibitory connections can develop stably by maintaining a balance between excitation and inhibition. The growth of long-range excitatory horizontal connections between layer 2/3 pyramidal cells is balanced against that of short-range disynaptic interneuronal connections. The growth of excitatory on-center connections from layer 6-to-4 is balanced against that of inhibitory interneuronal off-surround connections. These balanced connections interact via intracortical and intercortical feedback to realize properties of perceptual grouping, attention, and perceptual learning in the adult, and help to explain the observed variability in the number and temporal distribution of spikes emitted by cortical neurons. The model replicates cortical point spread functions and psychophysical data on the strength of real and illusory contours. The on-center off-surround layer 6-to-4 circuit enables top-clown attentional signals from area V2 to modulate, or attentionally prime, layer 4 cells in area Vl without fully activating them. This modulatory circuit also enables adult perceptual learning within cortical area Vl and V2 to proceed in a stable way.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

A Neural Model of How Horizontal and Interlaminar Connections of Visual Cortex Develop into Adult Circuits that Carry Out Perceptual Grouping and Learning

A neural model suggests how horizontal and interlaminar connections in visual cortical areas V1 and V2 develop within a laminar cortical architecture and give rise to adult visual percepts. The model suggests how mechanisms that control cortical development in the infant lead to properties of adult cortical anatomy, neurophysiology, and visual perception. The model clarifies how excitatory and inhibitory connections can develop stably by maintaining a balance between excitation and inhibition. The growth of long-range excitatory horizontal connections between layer 2/3 pyramidal cells is balanced against that of short-range disynaptie interneuronal connections. The growth of excitatory on-center connections from layer 6-to-1 is balanced against that of inhibitory interneuronal off-surround connections. These balanced connections interact via intracortical and intercortical feedback to realize properties of perceptual grouping, attention, and perceptual learning in the adult, and help to explain the observed variability in the number and temporal distribution of spikes emitted by cortical neurons. The model replicates cortical point spread functions and psychophysical data on the strength of real and illusory contours. The on-center off-surround layer 6-to-4 circuit enables top-down attentional signals from area V2 to modulate, or attentionally prime, layer 4 cells in area VI without fully activating them. This modulatory circuit also enables adult perceptual learning within cortical area, V1 and V2 to proceed in a stable way.Defense Advanced Research Projects Agency and Office of Naval Hesearch (N00014-95-l-0109); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657