waashk/dblp huggingface dataset:
a comparison of measures for detecting natural shapes in cluttered backgrounds. we propose a new measure of perceptual saliency and quantitatively compare its ability to detect natural shapes in cluttered backgrounds to five previously proposed measures. as defined in the new measure, the saliency of an edge is the fraction of closed random walks which contain that edge. the transition-probability matrix defining the random walk between edges is based on a distribution of natural shapes modeled by a stochastic motion. each of the saliency measures in our comparison is a function of a set of affinity values assigned to pairs of edges. although the authors of each measure define the affinity between a pair of edges somewhat differently, all incorporate the gestalt principles of good-continuation and proximity in some form. in order to make the comparison meaningful, we use a single definition of affinity and focus instead on the performance of the different functions for combining affinity values. the primary performance criterion is accuracy. we compute false-positive rates in classifying edges as signal or noise for a large set of test figures. in almost every case, the new measure significantly outperforms previous measures.
a closed-form solution to non-rigid shape and motion recovery. recovery of three dimensional (3d) shape and motion of non-static scenes from a monocular video sequence is important for applications like robot navigation and human computer interaction. if every point in the scene randomly moves, it is impossible to recover the non-rigid shapes. in practice, many non-rigid objects, e.g. the human face under various expressions, deform with certain structures. their shapes can be regarded as a weighted combination of certain shape bases. shape and motion recovery under such situations has attracted much interest. previous work on this problem (bregler, c., hertzmann, a., and biermann, h. 2000. in proc. int. conf. computer vision and pattern recognition; brand, m. 2001. in proc. int. conf. computer vision and pattern recognition; torresani, l., yang, d., alexander, g., and bregler, c. 2001. in proc. int. conf. computer vision and pattern recognition) utilized only orthonormality constraints on the camera rotations (rotation constraints). this paper proves that using only the rotation constraints results in ambiguous and invalid solutions. the ambiguity arises from the fact that the shape bases are not unique. an arbitrary linear transformation of the bases produces another set of eligible bases. to eliminate the ambiguity, we propose a set of novel constraints, basis constraints, which uniquely determine the shape bases. we prove that, under the weak-perspective projection model, enforcing both the basis and the rotation constraints leads to a closed-form solution to the problem of non-rigid shape and motion recovery. the accuracy and robustness of our closed-form solution is evaluated quantitatively on synthetic data and qualitatively on real video sequences.
shape and view independent reflectance map from multiple views. we consider the problem of estimating the 3d shape and reflectance properties of an object made of a single material from a set of calibrated views. to model the reflectance, we propose to use the view independent reflectance map (virm), which is a representation of the joint effect of the diffuse+specular bidirectional reflectance distribution function (brdf) and the environment illumination. the object shape is parameterized using a triangular mesh. we pose the estimation problem as minimizing the cost of matching input images, and the images synthesized using the shape and virm estimates. we show that by enforcing a constant value of virm as a global constraint, we can minimize the cost function by iterating between the virm and shape estimation. experimental results on both synthetic and real objects show that our algorithm can recover both the 3d shape and the diffuse/specular reflectance information. our algorithm does not require the light sources to be known or calibrated. the estimated virm can be used to predict the appearances of objects with the same material from novel viewpoints and under transformed illumination.
keypoint signatures for fast learning and recognition. statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. however, the training itself is usually relatively slow and performed offline. although methods have recently been proposed to train the classifier online, they can only learn a very limited number of new keypoints. this represents a handicap for real-time applications, such as simultaneous localization and mapping (slam), which require incremental addition of arbitrary numbers of keypoints as they become visible.in this paper, we overcome this limitation and propose a descriptor that can be learned online fast enough to handle virtually unlimited numbers of keypoints. it relies on the fact that if we train a randomized tree classifier to recognize a number of keypoints extracted from an image database, all other keypoints can be characterized in terms of their response to these classification trees. this signature is fast to compute and has a discriminative power that is comparable to that of the much slower sift descriptor.
some objects are more equal than others: measuring and predicting importance. we observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. we argue that a goal of visual recognition is, therefore, not only to detect and classify objects but also to associate with each a level of priority which we call `importance'. we propose a definition of importance and show how this may be estimated reliably from data harvested from human observers. we conclude by showing that a first-order estimate of importance may be computed from a number of simple image region measurements and does not require access to image meaning.
weakly supervised object localization with stable segmentations. multiple instance learning (mil) provides a framework for training a discriminative classifier from data with ambiguous labels. this framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. some recent work has explored the application of mil algorithms to the tasks of image categorization and natural scene classification. in this paper we extend these ideas in a framework that uses mil to recognize and localize objects in images. to achieve this we employ state of the art image descriptors and multiple stable segmentations. these components, combined with a powerful mil algorithm, form our object recognition system called milss. we show highly competitive object categorization results on the caltech dataset. to evaluate the performance of our algorithm further, we introduce the challenging landmarks-18 dataset, a collection of photographs of famous landmarks from around the world. the results on this new dataset show the great potential of our proposed algorithm.
using multiple hypotheses to improve depth-maps for multi-view stereo. we propose an algorithm to improve the quality of depth-maps used for multi-view stereo (mvs). many existing mvs techniques make use of a two stage approach which estimates depth-maps from neighbouring images and then merges them to extract a final surface. often the depth-maps used for the merging stage will contain outliers due to errors in the matching process. traditional systems exploit redundancy in the image sequence (the surface is seen in many views), in order to make the final surface estimate robust to these outliers. in the case of sparse data sets there is often insufficient redundancy and thus performance degrades as the number of images decreases. in order to improve performance in these circumstances it is necessary to remove the outliers from the depth-maps. we identify the two main sources of outliers in a top performing algorithm: (1) spurious matches due to repeated texture and (2) matching failure due to occlusion, distortion and lack of texture. we propose two contributions to tackle these failure modes. firstly, we store multiple depth hypotheses and use a spatial consistency constraint to extract the true depth. secondly, we allow the algorithm to return an unknown state when the a true depth estimate cannot be found. by combining these in a discrete label mrf optimisation we are able to obtain high accuracy depth-maps with low numbers of outliers. we evaluate our algorithm in a multi-view stereo framework and find it to confer state-of-the-art performance with the leading techniques, in particular on the standard evaluation sparse data sets.
geos: geodesic image segmentation. this paper presents geos, a new algorithm for the efficient segmentation of n-dimensional image and video data.the segmentation problem is cast as approximate energy minimization in a conditional random field. a new, parallel filtering operator built upon efficient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. an economical search algorithm finds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings.advantages include: i) computational efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. comparison with max-flow indicates up to 60 times greater computational efficiency as well as greater memory efficiency.geos is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. numerous results on interactive and automatic segmentation of photographs, video and volumetric medical image data are presented.
3d face recognition by local shape difference boosting. a new approach, called collective shape difference classifier (csdc), is proposed to improve the accuracy and computational efficiency of 3d face recognition. the csdc learns the most discriminative local areas from the pure shape difference map (psdm) and trains them as weak classifiers for assembling a collective strong classifier using the real-boosting approach. the psdm is established between two 3d face models aligned by a posture normalization procedure based on facial features. the model alignment is self-dependent, which avoids registering the probe face against every different gallery face during the recognition, so that a high computational speed is obtained. the experiments, carried out on the frgc v2 and bu-3dfe databases, yield rank-1 recognition rates better than 98%. each recognition against a gallery with 1000 faces only needs about 3.05 seconds. these two experimental results together with the high performance recognition on partial faces demonstrate that our algorithm is not only effective but also efficient.
velocity-guided tracking of deformable contours in three dimensional space. this paper presents a 3d active contour model for boundary detection and tracking of non-rigid objects, which applies stereo vision and motion analysis to the class of energy-minimizing deformable contour models, known as snakes. the proposed contour evolves in three-dimensional space in reaction to a 3d potential function, which is derived by projecting the contour onto the 2d stereo images. the potential function is augmented by a kinetic term, which is related to the velocity field along the contour. this term is used to guide the inter-image contour displacement. the incorporation of inter-frame velocity estimates in the tracking algorithm is especially important for contours which evolve in 3d space, where the added freedom of motion can easily result in loss of tracking. the proposed scheme incorporates local velocity information seamlessly in the snake model, with little computational overhead, and does not require exogenous computation of the optical flow or related quantities in each image. the resulting algorithm is shown to provide good tracking performance with only one iteration per frame, which provides a considerable advantage for real time operation.
viewpoint invariant pedestrian recognition with an ensemble of localized features. viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. this is likely due to the difficulty in matching two objects with unknown viewpoint and pose. this paper presents a method of performing viewpoint invariant pedestrian recognition using an efficiently and intelligently designed object representation, the ensemble of localized features (elf). instead of designing a specific feature by hand to solve the problem, we define a feature space using our intuition about the problem and let a machine learning algorithm find the best representation. we show how both an object class specific representation and a discriminative recognition model can be learned using the adaboost algorithm. this approach allows many different kinds of simple features to be combined into a single similarity function. the method is evaluated using a viewpoint invariant pedestrian recognition dataset and the results are shown to be superior to all previous benchmarks for both recognition and reacquisition of pedestrians.
a probabilistic approach to integrating multiple cues in visual tracking. this paper presents a novel probabilistic approach to integrating multiple cues in visual tracking. we perform tracking in different cues by interacting processes. each process is represented by a hidden markov model, and these parallel processes are arranged in a chain topology. the resulting linked hidden markov models naturally allow the use of particle filters and belief propagation in a unified framework. in particular, a target is tracked in each cue by a particle filter, and the particle filters in different cues interact via a message passing scheme. the general framework of our approach allows a customized combination of different cues in different situations, which is desirable from the implementation point of view. our examples selectively integrate four visual cues including color, edges, motion and contours. we demonstrate empirically that the ordering of the cues is nearly inconsequential, and that our approach is superior to other approaches such as independent integration and hierarchical integration in terms of flexibility and robustness.
multi-stage contour based detection of deformable objects. we present an efficient multi stage approach to detection of deformable objects in real, cluttered images given a single or few hand drawn examples as models. the method handles deformations of the object by first breaking the given model into segments at high curvature points. we allow bending at these points as it has been studied that deformation typically happens at high curvature points. the broken segments are then scaled, rotated, deformed and searched independently in the gradient image. point maps are generated for each segment that represent the locations of the matches for that segment. we then group k points from the point maps of k adjacent segments using a cost function that takes into account local scale variations as well as inter-segment orientations. these matched groups yield plausible locations for the objects. in the fine matching stage, the entire object contour in the localized regions is built from the k-segment groups and given a comprehensive score in a method that uses dynamic programming. an evaluation of our algorithm on a standard dataset yielded results that are better than published work on the same dataset. at the same time, we also evaluate our algorithm on additional images with considerable object deformations to verify the robustness of our method.
a generative shape regularization model for robust face alignment. in this paper, we present a robust face alignment system that is capable of dealing with exaggerating expressions, large occlusions, and a wide variety of image noises. the robustness comes from our shape regularization model, which incorporates constrained nonlinear shape prior, geometric transformation, and likelihood of multiple candidate landmarks in a three-layered generative model. the inference algorithm iteratively examines the best candidate positions and updates face shape and pose. this model can effectively recover sufficient shape details from very noisy observations. we demonstrate the performance of this approach on two public domain databases and a large collection of real-world face photographs.
learning spatial context: using stuff to find things. the sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the object. other types of objects of amorphous spatial extent (e.g., trees, sky), however, are more naturally classified based on texture or color. in this paper, we seek to combine recognition of these two types of objects into a system that leverages "context" toward improving detection. in particular, we cluster image regions based on their ability to serve as context for the detection of objects. rather than providing an explicit training set with region labels, our method automatically groups regions based on both their appearance and their relationships to the detections in the image. we show that our things and stuff (tas) context model produces meaningful clusters that are readily interpretable, and helps improve our detection ability over state-of-the-art detectors. we also present a method for learning the active set of relationships for a particular dataset. we present results on object detection in images from the pascal voc 2005/2006 datasets and on the task of overhead car detection in satellite images, demonstrating significant improvements over state-of-the-art detectors.
learning for optical flow using stochastic optimization. we present a technique for learning the parameters of a continuous-state markov random field (mrf) model of optical flow, by minimizing the training loss for a set of ground-truth images using simultaneous perturbation stochastic approximation (spsa). the use of spsa to directly minimize the training loss offers several advantages over most previous work on learning mrf models for low-level vision, which instead seek to maximize the likelihood of the data given the model parameters. in particular, our approach explicitly optimizes the error criterion used to evaluate the quality of the flow field, naturally handles missing data values in the ground truth, and does not require the kinds of approximations that current methods use to address the intractable nature of maximum-likelihood estimation for such problems. we show that our method achieves state-of-the-art results and requires only a very small number of training images. we also find that our method generalizes well to unseen data, including data with quite different characteristics than the training set.
recovering light directions and camera poses from a single sphere. this paper introduces a novel method for recovering both the light directions and camera poses from a single sphere. traditional methods for estimating light directions using spheres either assume both the radius and center of the sphere being known precisely, or they depend on multiple calibrated views to recover these parameters. it will be shown in this paper that the light directions can be uniquely determined from the specular highlights observed in a single view of a sphere without knowing or recovering the exact radius and center of the sphere. besides, if the sphere is being observed by multiple cameras, its images will uniquely define the translation vector of each camera from a common world origin centered at the sphere center. it will be shown that the relative rotations between the cameras can be recovered using two or more light directions estimated from each view. closed form solutions for recovering the light directions and camera poses are presented, and experimental results on both synthetic and real data show the practicality of the proposed method.
human activity recognition with metric learning. this paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. we show that our approach outperforms all state-of-the-art methods on numerous standard datasets for traditional action classification problem. furthermore, we demonstrate that our method not only can accurately label activities but also can reject unseen activities and can learn from few examples with high accuracy. we finally show that our approach works well on noisy youtube videos.
surface visibility probabilities in 3d cluttered scenes. many methods for 3d reconstruction in computer vision rely on probability models, for example, bayesian reasoning. here we introduce a probability model of surface visibilities in densely cluttered 3d scenes. the scenes consist of a large number of small surfaces distributed randomly in a 3d view volume. an example is the leaves or branches on a tree. we derive probabilities for surface visibility, instantaneous image velocity under egomotion, and binocular half---occlusions in these scenes. the probabilities depend on parameters such as scene depth, object size, 3d density, observer speed, and binocular baseline. we verify the correctness of our models using computer graphics simulations, and briefly discuss applications of the model to stereo and motion.
videocut: removing irrelevant frames by discovering the object of interest. we propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. we first hypothesize a number of candidate areas which possibly contain the object of interest, and then figure out which area(s) truly contain the object of interest. our method enjoys several favorable properties. first, compared to approaches where a single descriptor is used to describe a whole frame, each area's feature descriptor has the chance of genuinely describing the object of interest, hence it is less affected by background clutter. second, by considering the temporal continuity of a video instead of treating the frames as independent, we can hypothesize the location of the candidate areas more accurately. third, by infusing prior knowledge into the topic-motion model, we can precisely follow the trajectory of the object of interest. this allows us to largely reduce the number of candidate areas and hence reduce the chance of overfitting the data during learning. we demonstrate the effectiveness of the method by comparing it to several other semi-supervised learning approaches on challenging video clips.
an experimental comparison of discrete and continuous shape optimization methods. shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. in this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. the main contribution of this paper is to present a quantitative comparison of the reconstruction accuracy and computation times which allows to assess some of the strengths and limitations of both approaches. we also present a novel method to approximate length regularity in a graph cut based framework: instead of using pairwise terms we introduce higher order terms. these allow to represent a more accurate discretization of the l 2-norm in the length term.