Detecting shapes in noise: tuning characteristics of global shape mechanisms.

The proportion of signal elements embedded in noise needed to detect a signal is a standard tool for investigating motion perception. This paradigm was applied to the shape domain to determine how local information is pooled into a global percept. Stimulus arrays consisted of oriented Gabor elements that sampled the circumference of concentric radial frequency (RF) patterns. Individual Gabors were oriented tangentially to the shape (signal) or randomly (noise). In different conditions, signal elements were located randomly within the entire array or constrained to fall along one of the concentric contours. Coherence thresholds were measured for RF patterns with various frequencies (number of corners) and amplitudes ("sharpness" of corners). Coherence thresholds (about 10% = 15 elements) were lowest for circular shapes. Manipulating shape frequency or amplitude showed a range where thresholds remain unaffected (frequency ≤ RF4; amplitude ≤ 0.05). Increasing either parameter caused thresholds to rise. Compared to circles, thresholds increased by approximately four times for RF13 and five times for amplitudes of 0.3. Confining the signals to individual contours significantly reduced the number of elements needed to reach threshold (between 4 and 6), independent of the total number of elements on the contour or contour shape. Finally, adding external noise to the orientation of the elements had a greater effect on detection thresholds than adding noise to their position. These results provide evidence for a series of highly sensitive, shape-specific analysers which sum information globally but only from within specific annuli. These global mechanisms are tuned to position and orientation of local elements from which they pool information. The overall performance for arrays of elements can be explained by the sensitivity of multiple, independent concentric shape detectors rather than a single detector integrating information widely across space (e.g. Glass pattern detector)

A computational model of texture segmentation

An algorithm for finding texture boundaries in images is developed on the basis of a computational model of human texture perception. The model consists of three stages: (1) the image is convolved with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses; (2) inhibition, localized in space, within and among the neural response profiles results in the suppression of weak responses when there are strong responses at the same or nearby locations; and (3) texture boundaries are detected using peaks in the gradients of the inhibited response profiles. The model is precisely specified, equally applicable to grey-scale and binary textures, and is motivated by detailed comparison with psychophysics and physiology. It makes predictions about the degree of discriminability of different texture pairs which match very well with experimental measurements of discriminability in human observers. From a machine-vision point of view, the scheme is a high-quality texture-edge detector which works equally on images of artificial and natural scenes. The algorithm makes the use of simple local and parallel operations, which makes it potentially real-time

Preattentive texture discrimination with early vision mechanisms

We present a model of human preattentive texture perception. This model consists of three stages: (1) convolution of the image with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses modeling outputs of V1 simple cells, (2) inhibition, localized in space, within and among the neural-response profiles that results in the suppression of weak responses when there are strong responses at the same or nearby locations, and (3) texture-boundary detection by using wide odd-symmetric mechanisms. Our model can predict the salience of texture boundaries in any arbitrary gray-scale image. A computer implementation of this model has been tested on many of the classic stimuli from psychophysical literature. Quantitative predictions of the degree of discriminability of different texture pairs match well with experimental measurements of discriminability in human observers

Fluids mobilization in Arabia Terra, Mars: depth of pressurized reservoir from mounds self-similar clustering

Arabia Terra is a region of Mars where signs of past-water occurrence are recorded in several landforms. Broad and local scale geomorphological, compositional and hydrological analyses point towards pervasive fluid circulation through time. In this work we focus on mound fields located in the interior of three casters larger than 40 km (Firsoff, Kotido and unnamed crater 20 km to the east) and showing strong morphological and textural resemblance to terrestrial mud volcanoes and spring-related features. We infer that these landforms likely testify the presence of a pressurized fluid reservoir at depth and past fluid upwelling. We have performed morphometric analyses to characterize the mound morphologies and consequently retrieve an accurate automated mapping of the mounds within the craters for spatial distribution and fractal clustering analysis. The outcome of the fractal clustering yields information about the possible extent of the percolating fracture network at depth below the craters. We have been able to constrain the depth of the pressurized fluid reservoir between ~2.5 and 3.2 km of depth and hence, we propose that mounds and mounds alignments are most likely associated to the presence of fissure ridges and fluid outflow. Their process of formation is genetically linked to the formation of large intra-crater bulges previously interpreted as large scale spring deposits. The overburden removal caused by the impact crater formation is the inferred triggering mechanism for fluid pressurization and upwelling, that through time led to the formation of the intra-crater bulges and, after compaction and sealing, to the widespread mound fields in their surroundings

Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The d facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Our final combination outperforms the state-of-the-art without employing fine-tuning or ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin

Subset Warping: Rubber Sheeting with Cuts

Image warping, often referred to as "rubber sheeting" represents the deformation of a domain image space into a range image space. In this paper, a technique is described which extends the definition of a rubber-sheet transformation to allow a polygonal region to be warped into one or more subsets of itself, where the subsets may be multiply connected. To do this, it constructs a set of "slits" in the domain image, which correspond to discontinuities in the range image, using a technique based on generalized Voronoi diagrams. The concept of medial axis is extended to describe inner and outer medial contours of a polygon. Polygonal regions are decomposed into annular subregions, and path homotopies are introduced to describe the annular subregions. These constructions motivate the definition of a ladder, which guides the construction of grid point pairs necessary to effect the warp itself

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Multispectral iris recognition analysis: Techniques and evaluation

This thesis explores the benefits of using multispectral iris information acquired using a narrow-band multispectral imaging system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. While near-infrared imaging does give a very reasonable image of the iris texture, it only exploits a narrow band of spectral information. By incorporating other wavelength ranges (infrared, red, green, blue) in iris recognition systems, the reflectance and absorbance properties of the iris tissue can be exploited to enhance recognition performance. Furthermore, the impact of eye color on iris matching performance can be determined. In this work, a multispectral iris image acquisition system was assembled in order to procure data from human subjects. Multispectral images pertaining to 70 different eyes (35 subjects) were acquired using this setup. Three different iris localization algorithms were developed in order to isolate the iris information from the acquired images. While the first technique relied on the evidence presented by a single spectral channel (viz., near-infrared), the other two techniques exploited the information represented in multiple channels. Experimental results confirm the benefits of utilizing multiple channel information for iris segmentation. Next, an image enhancement technique using the CIE L*a*b* histogram equalization method was designed to improve the quality of the multispectral images. Further, a novel encoding method based on normalized pixel intensities was developed to represent the segmented iris images. The proposed encoding algorithm, when used in conjunction with the traditional texture-based scheme, was observed to result in very good matching performance. The work also explored the matching interoperability of iris images across multiple channels. This thesis clearly asserts the benefits of multispectral iris processing, and provides a foundation for further research in this topic

Isolation of Mercury-Resistant Endophytic and Rhizosphere Microorganisms from Grasses in Abandoned Gold Mining Area

There were about 900 hotspots of artisanal and small scale gold mining (ASGM) in Indonesia that recovered gold through amalgamation and cyanidation techniques. Amalgamation technique causes mercury (Hg) pollution to the soil. This study was a preliminary study that aimed to isolate Hg-resistant endophytic and rhizosphere microorganisms from pioneer grasses in the Hg-polluted soil. The most potential microorganism will be used for Hg phytoremediation in the future study. Pioneer grasses were collected from the abandoned gold mining area in Central Lombok Regency, West Nusa Tenggara. Total microorganisms were counted using Colony Forming Unit (CFU) or Standard Plate Count. The microorganism colony was characterized based on morphological characteristics. Hg-resistant endophytic and rhizosphere microorganisms were successfully isolated from pioneer grass (Cynodon dactylon and Eleusine indica) in the study site. The colonies of rhizosphere microorganisms were diverse morphologically compared to endophytic microorganisms based on the number of isolated microorganisms, 20 isolates and 17 isolates, respectively. The density of rhizosphere microorganisms was higher (96%) than endophytic microorganisms (4%). The density of rhizosphere bacteria and fungi were 47x103 and 2x103 CFU g-1, respectively. However, the density of endophytic bacteria and fungi were only 2x103 and 1x103 CFU g-1, respectively. Keywords: endophytic microorganism, Hg-resistant, microorganism density, rhizosphere microorganismTerdapat sekitar 900 titik pertambangan emas skala kecil (PESK) di Indonesia yang memperoleh emas melalui teknik amalgamasi dan sianidasi. Teknik amalgamasi menyebabkan pencemaran merkuri (Hg) di tanah. Penelitian ini merupakan penelitian pendahuluan (preliminary study) yang bertujuan untuk mengisolasi mikroorganisme endofit dan rhizosfer resisten Hg dari rumput pionir yang tumbuh di tanah yang tercemar Hg. Mikroorganisme paling berpotensi akan diaplikasikan pada fitoremediasi merkuri di penelitian selanjutnya. Sampel rumput pionir diambil dari lahan pertanian bekas kawasan pertambangan emas dengan teknik amalgamasi di Desa Bonjeruk, Kecamatan Jonggat, Kabupaten Lombok Tengah, Nusa Tenggara Barat. Total mikroorganisme dihitung menggunakan Colony Forming Unit (CFU) atau Standard Plate Count. Koloni mikroorganisme dikarakterisasi berdasarkan ciri morfologi. Mikroorganisme endofit dan rizosfer yang resisten Hg berhasil diisolasi dari rumput pionir (Cynodon dactylon dan Eleusine indica) di lokasi penelitian. Koloni mikroorganisme rizosfer sangat beragam secara morfologi dibandingkan dengan mikroorganisme endofit berdasarkan jumlah mikroorganisme terisolasi, berturut-turut 20 isolat dan 17 isolat. Kepadatan mikroorganisme rizosfer lebih tinggi (96%) dibandingkan mikroorganisme endofit (4%). Kepadatan bakteri dan jamur rizosfer masing-masing adalah 47x103 dan 2x103 CFU g-1 sedangkan kepadatan bakteri endofit dan jamur masing-masing hanya 2x103 dan 1x103 CFU g-1