waashk/dblp huggingface dataset:
robust fpga resynthesis based on fault-tolerant boolean matching. we present fpga logic synthesis algorithms for stochastic fault rate reduction in the presence of both permanent and transient defects. we develop an algorithm for fault tolerant boolean matching (ftbm), which exploits the flexibility of the lut configuration to maximize the stochastic yield rate for a logic function. using ftbm, we propose a robust resynthesis algorithm (rose) which maximizes stochastic yield rate for an entire circuit. finally, we show that existing plb (programmable logic block) templates for area-aware boolean matching and logic resynthesis are not effective for fault tolerance, and propose a new robust template with path re-convergence. compared to the state-of-the-art academic technology mapper berkeley abc, rose using the proposed robust plb template reduces the fault rate by 25% with 1% fewer luts, and increases mtbf (mean time between failures) by 31%, while preserving the optimal logic depth.
maps: multi-algorithm parallel circuit simulation. the emergence of multi-core and many-core processors has introduced new opportunities and challenges to eda research and development. while the availability of increasing parallel computing power holds new promise to address many computing challenges in cad, the leverage of hardware parallelism can only be possible with a new generation of parallel cad applications. in this paper, we propose a novel multi-algorithm parallel circuit simulation approach (maps) and its multi-core implementation to expedite one of the most fundamental cad applications: transistor-level transient circuit simulation. maps starts multiple simulation algorithms in parallel for a given simulation task. by properly synchronizing these algorithms on-the-fly, we exploit the diversity in simulation algorithms to achieve possibly superlinear overall speedup in transient simulation. in addition, our unique multi-algorithm framework allows unique safe exploration of simulation methods that are conventionally discarded due to convergence concerns. as a coarse grained parallel simulation approach, the implementation of maps demands a minimum of parallel programming effort and allows for reuse of existing serial simulation codes.
scalable and scalably-verifiable sequential synthesis. this paper describes an efficient implementation of sequential synthesis that uses induction to detect and merge sequentially-equivalent nodes. state-encoding, scan chains, and test vectors are essentially preserved. moreover, the sequential synthesis results are sequentially verifiable using an independent inductive prover similar to that used for synthesis, with guaranteed completeness. experiments with this sequential synthesis show effectiveness. when applied to a set of 20 industrial benchmarks ranging up to 26k registers and up to 53k 6-luts, average reductions in register and area are 12.9% and 13.1% respectively while delay is reduced by 1.4%. when applied to the largest academic benchmarks, an average reduction in both registers and area is more than 30%. the associated sequential verification is also scalable and runs about 2x slower than synthesis. the implementation is available in the synthesis and verification system abc.
temperature-aware test scheduling for multiprocessor systems-on-chip. increasing power densities due to process scaling, combined with high switching activity and poor cooling environments during testing, have the potential to result in high integrated circuit (ic) temperatures. this has the potential to damage ics and cause good ics to be discarded due to temperature-induced timing faults. we first study the power impact of scan chain testing for the iscas89 benchmarks. we find that the scan-chain test power consumption is 1.6x higher for at-speed testing than normal operating power consumption. we conclude that if the testing frequency is less than half of the normal frequency, then the testing power consumption may in fact be lower. however, due to differences in the cooling environments, the peak die temperatures may still be higher. second, we present an optimal formulation for minimal-duration temperature-constrained test scheduling. our results improve on the test schedule time of the best existing algorithm by 10.8% on average for a packaged ic thermal environment. we also present an efficient heuristic that generally produces the same results as the optimal algorithm, while requiring little cpu time, even for large problem instances.
dynamic feature ordering for efficient registration. existing sequential feature-based registration algorithms involving search typically either select features randomly (eg. the ransac[8] approach) or assume a predefined, intuitive ordering for the features (e.g. based on size or resolution). this paper presents a formal framework for computing an ordering for features which maximizes search efficiency.features are ranked according to matching ambiguity measure, and an algorithm is proposed which couples the feature selection with the parameter estimation, resulting in a dynamic feature ordering. the analysis is extended to template features where the matching is non-discrete and a sample-refinement process is proposed.the framework is demonstrated effectively on the localization of a person in an image, using a kinematic model with template features. different priors are used on the model parameters and the results demonstrate nontrivial variations in the optimal feature hierarchy.
mixtures of dynamic textures. a dynamic texture is a linear dynamical system used to model a single video as a sample from a spatio-temporal stochastic process. in this work, we introduce the mixture of dynamic textures, which models a collection of videos consisting of different visual processes as samples from a set of dynamic textures. we derive the em algorithm for learning a mixture of dynamic textures, and relate the learning algorithm and the dynamic texture mixture model to previous works. finally, we demonstrate the applicability of the proposed model to problems that have traditionally been challenging for computer vision.
good continuation of general 2d visual features: dual harmonic models and computational inference. good continuation is a fundamental principle of perceptual organization that guides the grouping of parts based on how they should succeed one another within coherent wholes. despite the general language that was used by the gestalt psychologists in phrasing this principle, computational work has focused almost exclusively on the study of curve-like structures. here we offer, for the first time, a rigorous generalization of good continuation to arbitrary visual structures that can be abstracted as scalar functions over the image plane. the differential geometry of these structures dictates that their good continuation should be based both on their value and on the geometry of their levelsets, which yield a coupled system of equations solvable for a formal model. we exhibit the resulting computation on shading and intensity functions, demonstrating how it eliminates spurious measurements while preserving both regular structure and singularities. related implementations could be applied to color channels, motion magnitude, and disparity signals.
globally convergent autocalibration. existing autocalibration techniques use numerical optimizationalgorithms that are prone to the problem of localminima. to address this problem, we have developeda method where an interval branch-and-bound method isemployed for numerical minimization. thanks to the propertiesof interval analysis this method is guaranteed to convergeto the global solution with mathematical certainty andarbitrary accuracy, and the only input information it requiresfrom the user is a set of point correspondences anda search box. the cost function is based on the huang-faugerasconstraint of the fundamental matrix. a recentlyproposed interval extension based on bernstein polynomialforms has been investigated to speed up the search for thesolution. finally, some experimental results on syntheticimages are presented.
towards direct recovery of shape and motion parameters from image sequences. a novel procedure is presented to construct image-domainfilters (receptive fields) that directly recover localmotion and shape parameters. these receptive fields arederived from training on image deformations that best discriminatebetween different shape and motion parameters.beginning with the construction of 1-d receptive fieldsthat detect local surface shape and motion parameterswithin cross sections, we show how the recovered shape andmotion model parameters are sufficient to produce local estimatesof time to collision.in general, filter pairs (receptive fields) can be synthesizedto perform or detect specific image deformations. atthe heart of the method is the use of a matrix to representimage deformation correspondence between individual pixelsof two views of a surface. the image correspondencematrix can be decomposed using singular value decompositionto yield a pair of corresponding receptive fields thatdetect image changes due to the deformation of interest.
active fixation using attentional shifts, affine resampling, and multiresolution search. presents a new approach for fixing two cameras at a single location in a 3d scene. fixation requires the detection of binocular disparity for a single point of interest in the scene so that vergence control can reduce that disparity to zero. most existing systems use area-based matching, since feature-based matching is computationally prohibitive. unfortunately, the area-based approach does not perform well when confronted with steeply inclined surfaces, occlusions, repeating patterns or featureless image regions. the method presented in this paper utilizes attentional shifts and affine resampling to combat these problems. these are integrated with adaptive window-size control and coarse-to-fine correlation-based searching. the effectiveness of the approach for complex scenes is demonstrated with several stereo image pairs.
self-calibrating a stereo head: an error analysis in the neighbourhood of degenerate configurations. we show that the self-calibration of a stereo head from corresponding points in an image pair is in certain circumstances prone to considerable error. a novel error analysis reveals that the automated determination of relative orientation and focal length is adversely affected when the cameras verge inwards a similar amount, and when the principal point locations have a horizontal error. this analysis is facilitated by the adoption of closed-form solutions for self-calibration from previous work of the authors. it is also shown that estimation of the fundamental matrix associated with a stereo head image pair is improved when a domain-specific parameterization and associated computational techniques are adopted. experiments conducted with such image pairs suggest that, given cognisance of sensitive configurations and adoption of the revised method of fundamental matrix estimation, robust reconstructions are attainable. this is demonstrated on the problem of metrically reconstructing a scene from two pairs of images obtained by an uncalibrated stereo head undergoing unknown ground-plane motion.
depth discontinuities by pixel-to-pixel stereo. an algorithm to detect depth discontinuities from a stereo pair of images is presented. the algorithm matches individual pixels in corresponding scanline pairs while allowing occluded pixels to remain unmatched, then propagates the information between scanlines by means of a fast postprocessor. the algorithm handles large untextured regions, uses a measure of pixel dissimilarity that is insensitive to image sampling, and prunes bad search nodes to increase the speed of dynamic programming. the computation is relatively fast, taking about 1.5 microseconds per pixel per disparityon a workstation. approximate disparity maps and precise depth discontinuities (along both horizontal and vertical boundaries) are shown for five stereo images containing textured, untextured, fronto-parallel, and slanted objects.
split aperture imaging for high dynamic range. most imaging sensors have limited dynamic range and hence are sensitive to only a part of the illumination range present in a natural scene. the dynamic range can be improved by acquiring multiple images of the same scene under different exposure settings and then combining them. in this paper, we describe a camera design for simultaneously acquiring multiple images. the cross-section of the incoming beam from a scene point is partitioned into as many parts as the required number of images. this is done by splitting the aperture into multiple parts and directing the beam exiting from each in a different direction using an assembly of mirrors. a sensor is placed in the path of each beam and exposure of each sensor is controlled either by appropriately setting its exposure parameter, or by splitting the incoming beam unevenly. the resulting multiple exposure images are used to construct a high dynamic range image. we have implemented a video-rate camera based on this design and the results obtained are presented.
face recognition in the presence of multiple illumination sources. most existing face recognition algorithms work well for controlled images but are quite susceptible to changes in illumination and pose. this has led to the rise of analysis-by-synthesis approaches due to their inherent potential to handle these external factors. though these approaches work quite well, most of them assume that the face is illuminated by a single light source which is usually not true in realistic conditions. in this paper, we propose an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources. the algorithm does not need to know the number of light sources and works extremely well even while recognizing faces illuminated by different number of light sources. results using this algorithm are reported on multiple-illumination datasets generated from pie [10] and yale face database b [5]. we also highlight the importance of the hard non-linearity in the lambert¿s law which is often ignored, probably to linearize the estimation process.
a framework for modeling appearance change in image sequences. image "appearance" may change over time due to a variety of causes such as 1) object or camera motion; 2) generic photometric events including variations in illumination (e.g. shadows) and specular reflections; and 3) "iconic changes" which are specific to the objects being viewed and include complex occlusion events and changes in the material properties ofthe objects. we propose a general framework for representing and recovering these "appearance changes" in an image sequence as a "mixture" of different causes. the approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion.
an algebraic approach to surface reconstruction from gradient fields. several important problems in computer vision such as shape from shading (sfs) and photometric stereo (ps) require reconstructing a surface from an estimated gradient field, which is usually non-integrable, i.e. have non-zero curl. we propose a purely algebraic approach to enforce integrability in discrete domain. we first show that enforcing integrability can be formulated as solving a single linear system ax = b over the image. in general, this system is under-determined. we show conditions under which the system can be solved and a method to get to those conditions based on graph theory. the proposed approach is non-iterative, has the important property of local errorconfinement and can be applied to several problems. results on sfs and ps demonstrate the applicability of our method.
tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. this paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. parametric flow models (for example affine) are popular for estimating motion in rigid scenes. we observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. these parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust and sadness can be recognized from the local parametric motions in the presence of significant head motion. the motion tracking and expression recognition approach performs with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.
camera calibration using spheres: a semi-definite programming approach. vision algorithms utilizing camera networks with a commonfield of view are becoming increasingly feasible andimportant. calibration of such camera networks is a challengingand cumbersome task. the current approaches forcalibration using planes or a known 3d target may not befeasible as these objects may not be simultaneously visiblein all the cameras. in this paper, we present a new algorithmto calibrate cameras using occluding contours of spheres.in general, an occluding contour of a sphere projects to anellipse in the image. our algorithm uses the projection ofthe occluding contours of three spheres and solves for theintrinsic parameters and the locations of the spheres. theproblem is formulated in the dual space and the parametersare solved for optimally and efficiently using semi-definiteprogramming. the technique is flexible, accurate and easyto use. in addition, since the contour of a sphere is simultaneouslyvisible in all the cameras, our approach cangreatly simplify calibration of multiple cameras with a commonfield of view. experimental results from computer simulateddata and real world data, both for a single cameraand multiple cameras, are presented.
actions as space-time shapes. human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. we regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. we adopt a recent approach [9] for analyzing 2d shapes and generalize it to deal with volumetric space-time action shapes. our method utilizes properties of the solution to the poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. we show that these features are useful for action recognition, detection and clustering. the method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action and low quality video.
detecting irregularities in images and in video. we address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. the term "irregular" depends on the context in which the "regular" or "valid" are defined. yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. we pose the problem of determining the validity of visual data as a process of constructing a puzzle: we try to compose a new observed image region or a new video segment ("the query") using chunks of data ("pieces of puzzle") extracted from previous visual examples ("the database"). regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. the problem is posed as an inference process in a probabilistic graphical model. we show applications of this approach to identifying saliency in images and video, and for suspicious behavior recognition.
recovery and tracking of continuous 3d surfaces from stereo data using a deformable dual-mesh. we propose a novel method for continuous 3d depth recovery and tracking using calibrated stereo. the method integrates stereo correspondence, surface reconstruction and tracking by using a new single deformable dual mesh optimization, resulting in simplicity, robustness and efficiency.in order to combine stereo correspondence and structure recovery, the method introduces an external energy function defined for a 3d volume based on cross-correlation between the stereo pairs. the internal energy functional of the deformable dual mesh imposes smoothness on the surfaces and it serves as a communication tool between the two meshes. under the forces produced by the energy terms, the dual mesh deforms to recover and track the 3d surface.the newly introduced dual-mesh model, which is one of the main contributions of this paper, makes the system robust against local minima and yet it is efficient. a coarse-to-fine minimization approach makes the system even more efficient. tracking is achieved by using the recovered surface as an initial position for the next time frame. although the system can effectively utilize initial surface positions and disparity data, they are not needed for a successful operation, which makes this system applicable to a wide range of areas. we present the results of a number of experiments on stereo human face and cloud images, which proves that our new method is very effective.
a theoretical limit on the number of effective pixels that can be optically resolved on a non-planar subject. in normal imaging systems, the depth of field is inversely proportional to the lens aperture. if we assume the system is diffraction limited, then the maximum resolution (i.e. pixels per mm) is proportional to the lens aperture. thus there is a tradeoff between depth of field and resolution. this tradeoff creates an upper limit on the number of pixels that can be resolved on a non-planar subject. this paper presents the theoretical limit on the number of pixels. the derivations of the limit shows that the limit is only a function of the subject size and depth. the subject distance, focal length, and sensor size do not matter. for small subjects the limit is well below the capabilities of modern imaging systems. for example a subject 15cm wide and 10cm deep can only be imaged with 300,000 pixels even though sensors with 10 times that many pixels are readily available. the resulting limit has obvious applications in machine vision, particularly when specifying optics and imaging sensors. experimental results are provided to validate the main result of the paper.