OpenRaiser/Intern-Atlas huggingface dataset:
Multiple View Image Reconstruction: A Harmonic Approach
Authors: Justin Domke, Yiannis Aloimonos
Venue: CVPR
Year: 2007

This paper presents a new constraint connecting the signals in multiple views of a surface. The constraint arises from a harmonic analysis of the geometry of the imaging process and it gives rise to a new technique for multiple view image reconstruction. Given several views of a surface from different positions, fundamentally different information is present in each image, owing to the fact that cameras measure the incoming light only after the application of a low-pass filter. Our analysis shows how the geometry of the imaging is connected to this filtering. This leads to a technique for constructing a single output image containing all the information present in the input images.
ROI-SEG: Unsupervised Color Segmentation by Combining Differently Focused Sub Results
Authors: Michael Donoser, Horst Bischof
Venue: CVPR
Year: 2007

This paper presents a novel unsupervised color segmentation scheme named ROI-SEG, which is based on the main idea of combining a set of different sub-segmentation results. We propose an efficient algorithm to compute sub-segmentations by an integral image approach for calculating Bhattacharyya distances and a modified version of the maximally stable extremal region (MSER) detector. The sub-segmentation algorithm gets a region-of-interest (ROI) as input and detects connected regions having similar color appearance as the ROI. We further introduce a method to identify ROIs representing the predominant color and texture regions of an image. Passing each of the identified ROIs to the sub-segmentation algorithm provides a set of different segmentations, which are then combined by analyzing a local quality criterion. The entire approach is fully unsupervised and does not need a priori information about the image scene. The method is compared to state-of-the-art algorithms on the Berkeley image database, where it shows competitive results at reduced computational costs.
A Bio-inspired Learning Approach for the Classification of Risk Zones in a Smart Space
Authors: Alessio Dore, Matteo Pinasco, Carlo S. Regazzoni
Venue: CVPR
Year: 2007

Learning from experience is a basic task of human brain that is not yet fulfilled satisfactorily by computers. Therefore, in recent years to cope with this issue, bio-inspired approaches has gathered the attention of several researchers. In this work a learning method is proposed based on a model derived from neurophysiological observations of the generation of the sense of self which is connected to the memorization of the interaction with external entity. The domain of application where this algorithm is employed, is a Cognitive Surveillance system which aims at detecting intruders and communicate guidance messages to a user (a guard) provided with a mobile device in order to chase him. The proposed method intends to allow the system to establish an efficient interaction with the user by sending messages only when necessary. To this end the zones of the monitored area are classified according to the probability that a change in pursuit strategy will occur by learning online the motion of the user and the intruder. The proposed algorithm has been tested on real world data demonstrating the capacity of learning this information to be used to tag the zones of the area under exam.
Crisp Weighted Support Vector Regression for robust single model estimation : application to object tracking in image sequences
Authors: Franck Dufrenois, Johan Colliez, Denis Hamad
Venue: CVPR
Year: 2007

Support Vector Regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with outliers and structured outliers in training data sets commonly encountered in computer vision applications. In this paper, we present a weighted version of SVM for regression. The proposed approach introduces an adaptive binary function that allows a dominant model from a degraded training dataset to be extracted. This binary function progressively separates inliers from outliers following a one-against-all decomposition. Experimental tests show the high robustness of the proposed approach against outliers and residual structured outliers. Next, we validate our algorithm for object tracking and for optic flow estimation.
Multiple-View Face Tracking For Modeling and Analysis Based On Non-Cooperative Video Imagery
Authors: Scott Von Duhn, Lijun Yin 0001, Myung Jin Ko, Terry Hung
Venue: CVPR
Year: 2007

3D face analysis has been researched intensively in recent decades. Most 3D data (so called range facial data) are obtained from 3D range imaging systems. Such data representations have been proven effective for face recognition in 3D space. However, obtaining such data requires subject cooperation in a constrained environment, which is not practical for many real applications of video surveillance. It is therefore in high demand to use regular video cameras to generate 3D face models for further classification. The goal of our research is to develop a method of tracking feature points on a face in multiple views in order to build 3D models of individual faces. We proposed a three-view based video tracking and model creation algorithm, which is based on the Active Appearance Model and a generic facial model. We will describe how to build useful individual models over time, and validate the created dynamic model sequences through the application of face recognition. Tracking multiple view fiducial points of a face in a time sequence can also be used for facial expression analysis. Our experiments demonstrated the feasibility of the proposed work.
Joint Optimization of Cascaded Classifiers for Computer Aided Detection
Authors: Murat Dundar, Jinbo Bi
Venue: CVPR
Year: 2007

The existing methods for offline training of cascade classifiers take a greedy search to optimize individual classifiers in the cascade, leading inefficient overall performance. We propose a new design of the cascaded classifier where all classifiers are optimized for the final objective function. The key contribution of this paper is the AND-OR framework for learning the classifiers in the cascade. In earlier work each classifier is trained independently using the examples labeled as positive by the previous classifiers in the cascade, and optimized to have the best performance for that specific local stage. The proposed approach takes into account the fact that an example is classified as positive by the cascade if it is labeled as positive by all the stages and it is classified as negative if it is rejected at any stage in the cascade. An offline training scheme is introduced based on the joint optimization of the classifiers in the cascade to minimize an overall objective function. We apply the proposed approach to the problem of automatically detecting polyps from multi-slice CT images. Our approach significantly speeds up the execution of the computer aided detection (CAD) system while yielding comparable performance with the current state-of-the-art, and also demonstrates favorable results over cascade AdaBoost both in terms of performance and online execution speed.
Multi-scale Features for Detection and Segmentation of Rocks in Mars Images
Authors: Heather Dunlop, David R. Thompson 0001, David Wettergreen
Venue: CVPR
Year: 2007

Geologists and planetary scientists will benefit from methods for accurate segmentation of rocks in natural scenes. However, rocks are poorly suited for current visual segmentation techniques - they exhibit diverse morphologies and have no uniform property to distinguish them from background soil. We address this challenge with a novel detection and segmentation method incorporating features from multiple scales. These features include local attributes such as texture, object attributes such as shading and two-dimensional shape, and scene attributes such as the direction of illumination. Our method uses a superpixel segmentation followed by region-merging to search for the most probable groups of superpixels. A learned model of rock appearances identifies whole rocks by scoring candidate superpixel groupings. We evaluate our method's performance on representative images from the Mars Exploration Rover catalog.
Multi-modal Person Identification in a Smart Environment
Authors: Hazim Kemal Ekenel, Mika Fischer, Qin Jin, Rainer Stiefelhagen
Venue: CVPR
Year: 2007

In this paper, we present a detailed analysis of multimodal fusion for person identification in a smart environment. The multi-modal system consists of a video-based face recognition system and a speaker identification system. We investigated different score normalization, modality weighting and modality combination schemes during the fusion of the individual modalities. We introduced two new modality weighting schemes, namely, the cumulative ratio of correct matches (CRCM) and distance-to-second-closest (DT2ND) measures. In addition, we also assessed the effects of the well-known score normalization and classifier combination methods on the identification performance. Experimental results obtained on the CLEAR 2007 evaluation corpus, which contains audio-visual recordings from different smart rooms, show that CRCM-based modality weighting improves the correct identification rates significantly.
Complex Salient Regions for Computer Vision Problems
Authors: Sergio Escalera, Petia Radeva, Oriol Pujol
Venue: CVPR
Year: 2007

The good of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to image transformations. We present a novel set of saliency feathers based on image singularities that takes into account the region content in terms of intensity and local structure. The region complexity is estimated by means of the entropy of the grey-level information; shape information is obtained by measuring the entropy of significant orientations. The regions are located in their representative scale and categorized by their complexity level. Thus, the regions are highly discriminable and less sensitive to confusion and false alalarm than the traditional approaches. We compare the novel complex salient regions with the state-of-the-art keypoint detectors. The presented interest points show robustness to a wide set of image transformations and high repeatability, as well as allows matching from different camera points of view. Beside. We show the temporal robustness of the novel salient regions in real video sequences, being potentially useful for matching, image retrieval, and object categorization problems.
Generative Graphical Models for Maneuvering Object Tracking and Dynamics Analysis
Authors: Xin Fan, Guoliang Fan
Venue: CVPR
Year: 2007

We study the challenging problem of maneuvering object tracking with unknown dynamics, i.e., forces or torque. We investigate the underlying causes of object kinematics, and propose a generative model approach that encodes the Newtonian dynamics for a rigid body by relating forces and torques with object's kinematics in a graphical model. This model also accommodates the physical constraints between maneuvering dynamics and object kinematics in a probabilistic form, allowing more accurate and efficient object tracking. Additionally, we develop a sequential Monte Carlo inference algorithm that is embedded with Markov Chain Monte Carlo (MCMC) steps to rejuvenate the path of particles. The proposed algorithm can estimate both maneuvering dynamics and object kinematics simultaneously. The experiments performed on both simulated and real-world data of ground vehicles show the robustness and effectiveness of the proposed graphical model-based approach along with the sampling-based inference algorithm.
Anonymous and Revocable Fingerprint Recognition
Authors: Faisal Farooq, Ruud M. Bolle, Tsai-Yang Jea, Nalini K. Ratha
Venue: CVPR
Year: 2007

Biometric identification has numerous advantages over conventional ID and password systems; however, the lack of anonymity and revocability of biometric templates is of concern. Several methods have been proposed to address these problems. Many of the approaches require a precise registration before matching in the anonymous domain. We introduce binary string representations of fingerprints that obviates the need for registration and can be directly matched. We describe several techniques for creating anonymous and revocable representations using these binary string representations. The match performance of these representations is evaluated using a large database of fingerprint images. We prove that given an anonymous representation, it is computationally infeasible to invert it to the original fingerprint, thereby preserving privacy. To the best of our knowledge, this is the first linear, anonymous and revocable fingerprint representation that is implicitly registered.
A Binning Scheme for Fast Hard Drive Based Image Search
Authors: Friedrich Fraundorfer, Henrik Stewénius, David Nistér
Venue: CVPR
Year: 2007

In this paper we investigate how to scale a content based image retrieval approach beyond the RAM limits of a single computer and to make use of its hard drive to store the feature database. The feature vectors describing the images in the database are binned in multiple independent ways. Each bin contains images similar to a representative prototype. Each binning is considered through two stages of processing. First, the prototype closest to the query is found. Second, the bin corresponding to the closest prototype is fetched from disk and searched completely. The query process is repeatedly performing these two stages, each time with a binning independent of the previous ones. The scheme cuts down the hard drive access significantly and results in a major speed up. An experimental comparison between the binning scheme and a raw search shows competitive retrieval quality.
Learning Object Material Categories via Pairwise Discriminant Analysis
Authors: Zhouyu Fu, Antonio Robles-Kelly
Venue: CVPR
Year: 2007

In this paper, we investigate linear discriminant analysis (LDA) methods for multiclass classification problems in hyperspectral imaging. We note that LDA does not consider pairwise relations between different classes, it rather assumes equal within and between-class scatter matrices. As a result, we present a pairwise discriminant analysis algorithm for learning class categories. Our pairwise linear discriminant analysis measures the separability of two classes making use of the class centroids and variances. Our approach is based upon a novel cost function with unitary constraints based on the aggregation of pairwise costs for binary classes. We view the minimisation of this cost function as an unconstrained optimisation problem over a Grassmann manifold and solve using a projected gradient method. Our approach does not require matrix inversion operations and, therefore, does not suffer of stability problems for small training sets. We demonstrate the utility of our algorithm for purposes of learning material catergories in hyperspectral images.
Accurate, Dense, and Robust Multi-View Stereopsis
Authors: Yasutaka Furukawa, Jean Ponce
Venue: CVPR
Year: 2007

This paper proposes a novel algorithm for calibrated multi-view stereopsis that outputs a (quasi) dense set of rectangular patches covering the surfaces visible in the input images. This algorithm does not require any initialization in the form of a bounding volume, and it detects and discards automatically outliers and obstacles. It does not perform any smoothing across nearby features, yet is currently the top performer in terms of both coverage and accuracy for four of the six benchmark datasets presented in [20]. The keys to its performance are effective techniques for enforcing local photometric consistency and global visibility constraints. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these to nearby pixel correspondences before using visibility constraints to filter away false matches. A simple but effective method for turning the resulting patch model into a mesh appropriate for image-based modeling is also presented. The proposed approach is demonstrated on various datasets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in different places in multiple images of a static structure of interest.
Using Group Prior to Identify People in Consumer Images
Authors: Andrew C. Gallagher, Tsuhan Chen
Venue: CVPR
Year: 2007

While face recognition techniques have rapidly advanced in the last few years, most of the work is in the domain of security applications. For consumer imaging applications, person recognition is an important tool that is useful for searching and retrieving images from a personal image collection. It has been shown that when recognizing a single person in an image, a maximum likelihood classifier requires the prior probability for each candidate individual. In this paper, we extend this idea and describe the benefits of using a group prior for identifying people in consumer images with multiple people. The group prior describes the probability of a group of individuals appearing together in an image. In our application, we have a subset of ambiguously labeled images for a consumer image collection, where we seek to identify all of the people in the collection. We describe a simple algorithm for resolving the ambiguous labels. We show that despite errors in resolving ambiguous labels, useful classifiers can be trained with the resolved labels. Recognition performance is further improved with a group prior learned from the ambiguous labels. In summary, by modeling the relationships between the people with the group prior, we improve classification performance.
Active Aperture Control and Sensor Modulation for Flexible Imaging
Authors: Chunyu Gao, Narendra Ahuja, Hong Hua
Venue: CVPR
Year: 2007

In the paper, we describe an optical system which is capable of providing external access to both the sensor and the lens aperture (i.e., projection center) of a conventional camera. The proposed optical system is attached in front of the camera, and is the equivalent of adding externally accessible intermediate image plane and projection center. The system offers controls of the response of each pixel which could be used to realize many added imaging functions, such as high dynamic range imaging, image modulation, and optical computation. The ability to access the optical center could enable a wide variety of applications by simply allowing manipulation of the geometric properties of the optical center. For instance, panoramic imaging can be implemented by rotating a planar mirror about the camera axis; and small base-line stereo can be implemented by shifting the camera center. We have implemented a bench setup to demonstrate some of these functions. The experimental results are included.
Discriminant Interest Points are Stable
Authors: Dashan Gao 0001, Nuno Vasconcelos
Venue: CVPR
Year: 2007

A study of the performance of recently introduced discriminant methods for interest point detection [6,14] is presented. It has been previously shown that the resulting interest points are more informative for object recognition than those produced by the detectors currently used in computer vision. Little is, however, known about the properties of discriminant points with respect to the metrics, such as repeatability, that have been traditionally used to evaluate interest point detection. A thorough experimental evaluation of the stability of discriminant points is presented, and this stability compared to those of four popular methods. In particular, we consider image correspondence under geometric and photometric transformations, and extend the experimental protocol proposed by Mikolajczyk et al. [13] for the evaluation of stability with respect to such transformations. The extended protocol is suitable for the evaluation of both bottom-up and top-down (learned) detectors. It is shown that the stability of discriminant interest points is comparable, and frequently superior, to those of interest points produced by various currently popular techniques.
A Nine-point Algorithm for Estimating Para-Catadioptric Fundamental Matrices
Authors: Christopher Geyer, Henrik Stewénius
Venue: CVPR
Year: 2007

We present a minimal-point algorithm for finding fundamental matrices for catadioptric cameras of the parabolic type. Central catadioptric cameras-an optical combination of a mirror and a lens that yields an imaging device equivalent within hemispheres to perspective cameras-have found wide application in robotics, tele-immersion and providing enhanced situational awareness for remote operation. We use an uncalibrated structure-from-motion framework developed for these cameras to consider the problem of estimating the fundamental matrix for such cameras. We present a solution that can compute the para-catadioptirc fundamental matrix with nine point correspondences, the smallest number possible. We compare this algorithm to alternatives and show some results of using the algorithm in conjunction with random sample consensus (RANSAC).
An Optimal Reduced Representation of a MoG with Applicatios to Medical Image Database Classification
Authors: Jacob Goldberger, Hayit Greenspan, Jeremie Dreyfuss
Venue: CVPR
Year: 2007

This work focuses on a general framework for image categorization, classification and retrieval that may be appropriate for medical image archives. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (MoG) along with information-theoretic image matching measures (KL). A category model is obtained by learning a reduced model from all the images in the category. We propose a novel algorithm for learning a reduced representation of a MoG, that is based on the unscented-transform. The superiority of the proposed method is validated on both simulation experiments and categorization of a real medical image database.
A New Method for Object Tracking Based on Regions Instead of Contours
Authors: Nicolás Amézquita Gómez, René Alquézar, Francesc Serratosa
Venue: CVPR
Year: 2007

This paper presents a new method for object tracking in video sequences that is especially suitable in very noisy environments. In such situations, segmented images from one frame to the next one are usually so different that it is very hard or even impossible to match the corresponding regions or contours of both images. With the aim of tracking objects in these situations, our approach has two main characteristics. On one hand, we assume that the tracking approaches based on contours cannot be applied, and therefore, our system uses object recognition results computed from regions (specifically, colour spots from segmented images). On the other hand, we discard to match the spots of consecutive segmented images and, consequently, the methods that represent the objects by structures such as graphs or skeletons, since the structures obtained may be too different in consecutive frames. Thus, we represent the location of tracked objects through images of probabilities that are updated dynamically using both recognition and tracking results in previous steps. From these probabilities and a simple prediction of the apparent motion of the object in the image, a binary decision can be made for each pixel and abject.