A feedback-based algorithm forMotion analysis with application to object tracking

We present a motion detection algorithm which detects direction of motion at sufficient number of points and thus segregates the edge image into clusters of coherently moving points. Unlike most algorithms for motion analysis, we do not estimate magnitude of velocity vectors or obtain dense motion maps. The motivation is that motion direction information at a number of points seems to be sufficient to evoke perception of motion and hence should be useful in many image processing tasks requiring motion analysis. The algorithm essentially updates the motion at previous time using the current image frame as input in a dynamic fashion. One of the novel features of the algorithm is the use of some feedback mechanism for evidence segregation. This kind of motion analysis can identify regions in the image that are moving together coherently, and such information could be sufficient for many applications that utilize motion such as segmentation, compression, and tracking. We present an algorithm for tracking objects using our motion information to demonstrate the potential of this motion detection algorithm.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

Formation of ω-Al7Cu2Fe phase during laser processing of quasicrystal-forming Al-Cu-Fe alloy

The formation of an ω-Al7Cu2Fe phase during laser cladding of quasicrystal-forming Al65Cu23.3Fe11.7 alloy on a pure aluminium substrate is reported. This phase is found to nucleate at the periphery of primary icosahedral-phase particles. A large number of ω-phase particles form an envelope around the icosahedral phase. On the outer side, they form an interface with an agr-Al solid solution. Detailed transmission electron microscopic observations show that the ω phase exhibits an orientation relationship with the icosahedral phase. Analysis of experimental results suggests that the ω phase forms by precipitation on an icosahedral phase by heterogeneous nucleation and grows into the aluminium-rich melt until supersaturation is exhausted. The microstructural observations are explained in terms of available models of phase transformations.

Experiments on three-dimensional wire-frame object recognition

Some experimental results on the recognition of three-dimensional wire-frame objects are presented. In order to overcome the limitations of a recent model, which employs radial basis functions-based neural networks, we have proposed a hybrid learning system for object recognition, featuring: an optimization strategy (simulated annealing) in order to avoid local minima of an energy functional; and an appropriate choice of centers of the units. Further, in an attempt to achieve improved generalization ability, and to reduce the time for training, we invoke the principle of self-organization which utilises an unsupervised learning algorithm.

Object Tracking Using Motion Direction Detection

We present an algorithm for tracking objects in a video sequence, based on a novel approach for motion detection. We do not estimate the velocity �eld. In-stead we detect only the direction of motion at edge points and thus isolate sets of points which are moving coherently. We use a Hausdor� distance based matching algorithm to match point sets in local neighborhood and thus track objects in a video sequence. We show through some examples the e�ectiveness of the algo- rithm.

Microstructure and texture evolution during sub-transus thermomechanical processing of Ti-6Al-4V-0.1B alloy: part I. hot rolling in (alpha plus beta) phase field

In the current study, the evolution of microstructure and texture has been studied for Ti-6Al-4V-0.1B alloy during sub-transus thermomechanical processing. This part of the work deals with the deformation response of the alloy by rolling in the (alpha + beta) phase field. The (alpha + beta) annealing behavior of the rolled specimen is communicated in part II. Rolled microstructures of the alloys exhibit either kinked or straight alpha colonies depending on their orientations with respect to the principal rolling directions. The Ti-6Al-4V-0.1B alloy shows an improved rolling response compared with the alloy Ti-6Al-4V because of smaller alpha lamellae size, coherency of alpha/beta interfaces, and multiple slip due to orientation factors. Accelerated dynamic globularization for this alloy is similarly caused by the intralamellar transverse boundary formation via multiple slip and strain accumulation at TiB particles. The (0002)(alpha) pole figures of rolled Ti-6Al-4V alloy shows ``TD splitting'' at lower rolling temperatures because of strong initial texture. Substantial beta phase mitigates the effect of starting texture at higher temperature so that ``RD splitting'' characterizes the basal pole figure. Weak starting texture and easy slip transfer for Ti-6Al-4V-0.1B alloy produce simultaneous TD and RD splittings in basal pole figures at all rolling temperatures.

Fragment-based real-time object tracking: a sparse representation approach

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l(1) framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l(1) regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach.

Influence of seed layer orientation on the growth and physical properties of SnS nanostructures

Stoichiometric tin (II) sulfide (SnS) nano-structures were synthesized on SnS(010)/glass substrates using a simple and low-temperature chemical solution method, and their physical properties were investigated. The as-synthesized SnS nanostructures exhibited orthorhombic crystal structure and most of the nanocrystals are preferentially oriented along the <010> direction. These nanostructures showed p-type electrical conductivity and high electrical resistivity of 93 Omega cm. SnS nanostructures exhibited a direct optical band gap of 1.43 eV. While increasing the surrounding temperature from 20 to 150 degrees C, the electrical resistivity of the structures decreased and exhibited the activation energy of 0.28 eV.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

Object attributes combine additively in visual search

We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.

SALIENT OBJECT DETECTION VIA OBJECTNESS MEASURE

Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth and accurate saliency maps. We propose a novel saliency measure called `foreground connectivity' which determines how tightly a pixel or a region is connected to the estimated foreground. We use the values assigned by this measure as foreground weights and integrate these in an optimization framework to obtain the final saliency maps. We extensively evaluate the proposed approach on two benchmark databases and demonstrate that the results obtained are better than the existing state of the art approaches.

Validation of processing maps for 304L stainless steel using hot forging, rolling and extrusion

The development of a microstructure in 304L stainless steel during industrial hot-forming operations, including press forging (mean strain rate of 0.15 s(-1)), rolling/extrusion (2-5 s(-1)), and hammer forging (100 s(-1)) at different temperatures in the range 600-1200 degrees C, was studied with a view to validating the predictions of the processing map. The results have shown that excellent correlation exists between the regimes exhibited by the map and the product microstructures. 304L stainless steel exhibits instability bands when hammer forged at temperatures below 1100 degrees C, rolled/extruded below 1000 degrees C, or press forged below 800 degrees C. All of these conditions must be avoided in mechanical processing of the material. On the other hand, ideally, the material may be rolled, extruded, or press forged at 1200 degrees C to obtain a defect-free microstructure.

Mechanical processing and microstructural control in hot working of hot isostatically pressed P/M IN-100 superalloy

The hot deformation behavior of hot isostatically pressed (HIPd) P/M IN-100 superalloy has been studied in the temperature range 1000-1200 degrees C and strain rate range 0.0003-10 s(-1) using hot compression testing. A processing map has been developed on the basis of these data and using the principles of dynamic materials modelling. The map exhibited three domains: one at 1050 degrees C and 0.01 s(-1), with a peak efficiency of power dissipation of approximate to 32%, the second at 1150 degrees C and 10 s(-1), with a peak efficiency of approximate to 36% and the third at 1200 degrees C and 0.1 s(-1), with a similar efficiency. On the basis of optical and electron microscopic observations, the first domain was interpreted to represent dynamic recovery of the gamma phase, the second domain represents dynamic recrystallization (DRX) of gamma in the presence of softer gamma', while the third domain represents DRX of the gamma phase only. The gamma' phase is stable upto 1150 degrees C, gets deformed below this temperature and the chunky gamma' accumulates dislocations, which at larger strains cause cracking of this phase. At temperatures lower than 1080 degrees C and strain rates higher than 0.1 s(-1), the material exhibits flow instability, manifested in the form of adiabatic shear bands. The material may be subjected to mechanical processing without cracking or instabilities at 1200 degrees C and 0.1 s(-1), which are the conditions for DRX of the gamma phase.

Use of microwaves for the synthesis and processing of materials

An overview of the synthesis of materials under microwave irradiation has been presented based on the work performed recently. A variety of reactions such as direct combination, carbothermal reduction, carbidation and nitridation have been described. Examples of microwave preparation of glasses are also presented. Great advantages of fast, clean and reduced reaction temperature of microwave methods are emphasized. The example of ZrO2-CeO2 ceramics has been used show the extraordinarily fast and effective sintering which occurs in microwave irradiation.

High-temperature deformation processing of Ti-24Al-20Nb

Power dissipation maps have been generated in the temperature range of 900 degrees C to 1150 degrees C and strain rate range of 10(-3) to 10 s(-1) for a cast aluminide alloy Ti-24Al-20Nb using dynamic material model. The results define two distinct regimes of temperature and strain rate in which efficiency of power dissipation is maximum. The first region, centered around 975 degrees C/0.1 s(-1), is shown to correspond to dynamic recrystallization of the alpha(2) phase and the second, centered around 1150 degrees C/0.001 s(-1), corresponds to dynamic recovery and superplastic deformation of the beta phase. Thermal activation analysis using the power law creep equation yielded apparent activation energies of 854 and 627 kJ/mol for the first and second regimes, respectively. Reanalyzing the data by alternate methods yielded activation energies in the range of 170 to 220 kJ/mol and 220 to 270 kJ/mol for the first and second regimes, respectively. Cross slip was shown to constitute the activation barrier in both cases. Two distinct regimes of processing instability-one at high strain rates and the other at the low strain rates in the lower temperature regions-have been identified, within which shear bands are formed.