Coarse-level object recognition using interlevel products of complex wavelets

This paper introduces the Interlevel Product (ILP) which is a transform based upon the Dual-Tree Complex Wavelet. Coefficients of the ILP have complex values whose magnitudes indicate the amplitude of multilevel features, and whose phases indicate the nature of these features (e.g. ridges vs. edges). In particular, the phases of ILP coefficients are approximately invariant to small shifts in the original images. We accordingly introduce this transform as a solution to coarse scale template matching, where alignment concerns between decimation of a target and decimation of a larger search image can be mitigated, and computational efficiency can be maintained. Furthermore, template matching with ILP coefficients can provide several intuitive "near-matches" that may be of interest in image retrieval applications. © 2005 IEEE.

Multiscale object features from clustered complex wavelet coefficients

This paper introduces a method by which intuitive feature entities can be created from ILP (InterLevel Product) coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales, derived from dual-tree complex wavelets, whose phases indicate the presence of different feature types (edges and ridges). We use an Expectation-Maximization algorithm to cluster large ILP coefficients that are spatially adjacent and similar in phase. We then demonstrate the relationship that these clusters possess with respect to observable image content, and conclude with a look at potential applications of these clusters, such as rotation- and scale-invariant object recognition. © 2005 IEEE.

The Effects of Visual Object Size in The Depth Perception in KM Mouse

Whether mice perceive the depth of space dependent on the visual size of object targets was explored when visual cues such as perspective and partial occlusion in space were excluded. A mouse was placed on a platform the height of which is adjustable. The platform located inside a box in which all other walls were dark exception its bottom through that light was projected as a sole visual cue. The visual object cue was composed of 4x4 grids to allow a mouse estimating the distance of the platform relative to the grids. Three sizes of grids reduced in a proportion of 2/3 and seven distances with an equal interval between the platform and the grids at the bottom were applied in the experiments. The duration of a mouse staying on the platform at each height was recorded when the different sizes of the grids were presented randomly to test whether the Judgment of the mouse for the depth of the platform from the bottom was affected by the size information of the visual target. The results from all conditions of three object sizes show that time of mice staying on the platform became longer with the increase in height. In distance of 20 similar to 30 cm, the mice did not use the size information of a target to judge the depth, while mainly used the information of binocular disparity. In distance less than 20 cm or more than 30 cm, however, especially in much higher distance 50 cm, 60 cm and 70 cm, the mice were able to use the size information to do so in order to compensate the lack of binocular disparity information from both eyes. Because the mice have only 1/3 of the visual field that is binocular. This behavioral paradigm established in the current study is a useful model and can be applied to the experiments using transgenic mouse as an animal model to investigate the relationships between behaviors and gene functions.

Marine Vertebrates and Low Frequency Sound: Technical Report for LFA EIS

Over the past 50 years, economic and technological developments have dramatically increased the human contribution to ambient noise in the ocean. The dominant frequencies of most human-made noise in the ocean is in the low-frequency range (defined as sound energy below 1000Hz), and low-frequency sound (LFS) may travel great distances in the ocean due to the unique propagation characteristics of the deep ocean (Munk et al. 1989). For example, in the Northern Hemisphere oceans low-frequency ambient noise levels have increased by as much as 10 dB during the period from 1950 to 1975 (Urick 1986; review by NRC 1994). Shipping is the overwhelmingly dominant source of low-frequency manmade noise in the ocean, but other sources of manmade LFS including sounds from oil and gas industrial development and production activities (seismic exploration, construction work, drilling, production platforms), and scientific research (e.g., acoustic tomography and thermography, underwater communication). The SURTASS LFA system is an additional source of human-produced LFS in the ocean, contributing sound energy in the 100-500 Hz band. When considering a document that addresses the potential effects of a low-frequency sound source on the marine environment, it is important to focus upon those species that are the most likely to be affected. Important criteria are: 1) the physics of sound as it relates to biological organisms; 2) the nature of the exposure (i.e. duration, frequency, and intensity); and 3) the geographic region in which the sound source will be operated (which, when considered with the distribution of the organisms will determine which species will be exposed). The goal in this section of the LFA/EIS is to examine the status, distribution, abundance, reproduction, foraging behavior, vocal behavior, and known impacts of human activity of those species may be impacted by LFA operations. To focus our efforts, we have examined species that may be physically affected and are found in the region where the LFA source will be operated. The large-scale geographic location of species in relation to the sound source can be determined from the distribution of each species. However, the physical ability for the organism to be impacted depends upon the nature of the sound source (i.e. explosive, impulsive, or non-impulsive); and the acoustic properties of the medium (i.e. seawater) and the organism. Non-impulsive sound is comprised of the movement of particles in a medium. Motion is imparted by a vibrating object (diaphragm of a speaker, vocal chords, etc.). Due to the proximity of the particles in the medium, this motion is transmitted from particle to particle in waves away from the sound source. Because the particle motion is along the same axis as the propagating wave, the waves are longitudinal. Particles move away from then back towards the vibrating source, creating areas of compression (high pressure) and areas of rarefaction (low pressure). As the motion is transferred from one particle to the next, the sound propagates away from the sound source. Wavelength is the distance from one pressure peak to the next. Frequency is the number of waves passing per unit time (Hz). Sound velocity (not to be confused with particle velocity) is the impedance is loosely equivalent to the resistance of a medium to the passage of sound waves (technically it is the ratio of acoustic pressure to particle velocity). A high impedance means that acoustic particle velocity is small for a given pressure (low impedance the opposite). When a sound strikes a boundary between media of different impedances, both reflection and refraction, and a transfer of energy can occur. The intensity of the reflection is a function of the intensity of the sound wave and the impedances of the two media. Two key factors in determining the potential for damage due to a sound source are the intensity of the sound wave and the impedance difference between the two media (impedance mis-match). The bodies of the vast majority of organisms in the ocean (particularly phytoplankton and zooplankton) have similar sound impedence values to that of seawater. As a result, the potential for sound damage is low; organisms are effectively transparent to the sound – it passes through them without transferring damage-causing energy. Due to the considerations above, we have undertaken a detailed analysis of species which met the following criteria: 1) Is the species capable of being physically affected by LFS? Are acoustic impedence mis-matches large enough to enable LFS to have a physical affect or allow the species to sense LFS? 2) Does the proposed SURTASS LFA geographical sphere of acoustic influence overlap the distribution of the species? Species that did not meet the above criteria were excluded from consideration. For example, phytoplankton and zooplankton species lack acoustic impedance mis-matches at low frequencies to expect them to be physically affected SURTASS LFA. Vertebrates are the organisms that fit these criteria and we have accordingly focused our analysis of the affected environment on these vertebrate groups in the world’s oceans: fishes, reptiles, seabirds, pinnipeds, cetaceans, pinnipeds, mustelids, sirenians (Table 1).

Biologically-inspired object recognition system with features from complex wavelets

In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in [2] (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.

Silhouette-based object phenotype recognition using 3D shape priors

This paper tackles the novel challenging problem of 3D object phenotype recognition from a single 2D silhouette. To bridge the large pose (articulation or deformation) and camera viewpoint changes between the gallery images and query image, we propose a novel probabilistic inference algorithm based on 3D shape priors. Our approach combines both generative and discriminative learning. We use latent probabilistic generative models to capture 3D shape and pose variations from a set of 3D mesh models. Based on these 3D shape priors, we generate a large number of projections for different phenotype classes, poses, and camera viewpoints, and implement Random Forests to efficiently solve the shape and pose inference problems. By model selection in terms of the silhouette coherency between the query and the projections of 3D shapes synthesized using the galleries, we achieve the phenotype recognition result as well as a fast approximate 3D reconstruction of the query. To verify the efficacy of the proposed approach, we present new datasets which contain over 500 images of various human and shark phenotypes and motions. The experimental results clearly show the benefits of using the 3D priors in the proposed method over previous 2D-based methods. © 2011 IEEE.

Correlating Multiple 2D Vision Trackers for 3D Object Tracking on Construction Sites

Tracking methods have the potential to retrieve the spatial location of project related entities such as personnel and equipment at construction sites, which can facilitate several construction management tasks. Existing tracking methods are mainly based on Radio Frequency (RF) technologies and thus require manual deployment of tags. On construction sites with numerous entities, tags installation, maintenance and decommissioning become an issue since it increases the cost and time needed to implement these tracking methods. To address these limitations, this paper proposes an alternate 3D tracking method based on vision. It operates by tracking the designated object in 2D video frames and correlating the tracking results from multiple pre-calibrated views using epipolar geometry. The methodology presented in this paper has been implemented and tested on videos taken in controlled experimental conditions. Results are compared with the actual 3D positions to validate its performance.

Testing of Depth-Encoded Hough Voting for Infrastructure Object Detection

The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.

Monte Carlo-Based Bayesian group object tracking and causal reasoning

We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts. © 2013 Springer-Verlag Berlin Heidelberg.

Comparing crowding in human and ideal observers.

A visual target is more difficult to recognize when it is surrounded by other, similar objects. This breakdown in object recognition is known as crowding. Despite a long history of experimental work, computational models of crowding are still sparse. Specifically, few studies have examined crowding using an ideal-observer approach. Here, we compare crowding in ideal observers with crowding in humans. We derived an ideal-observer model for target identification under conditions of position and identity uncertainty. Simulations showed that this model reproduces the hallmark of crowding, namely a critical spacing that scales with viewing eccentricity. To examine how well the model fits quantitatively to human data, we performed three experiments. In Experiments 1 and 2, we measured observers' perceptual uncertainty about stimulus positions and identities, respectively, for a target in isolation. In Experiment 3, observers identified a target that was flanked by two distractors. We found that about half of the errors in Experiment 3 could be accounted for by the perceptual uncertainty measured in Experiments 1 and 2. The remainder of the errors could be accounted for by assuming that uncertainty (i.e., the width of internal noise distribution) about stimulus positions and identities depends on flanker proximity. Our results provide a mathematical restatement of the crowding problem and support the hypothesis that crowding behavior is a sign of optimality rather than a perceptual defect.