31,718 research outputs found
Mean-shift analysis for image and video applications
Cataloged from PDF version of article.In this thesis, image and video analysis algorithms are developed. Tracking moving
objects in video have important applications ranging from CCTV (Closed Circuit
Television Systems) to infrared cameras. In current CCTV systems, 80% of
the time, it is impossible to recognize suspects from the recorded scenes. Therefore,
it is very important to get a close shot of a person so that his or her face
is recognizable. To take high-resolution pictures of moving objects, a pan-tiltzoom
camera should automatically follow moving objects and record them. In
this thesis, a mean-shift based moving object tracking algorithm is developed. In
ordinary mean-shift tracking algorithm a color histogram or a probability density
function (pdf) estimated from image pixels is used to represent the moving
object. In our case, a joint-probability density function is used to represent the
object. The joint-pdf is estimated from the object pixels and their wavelet transform
coefficients. In this way, relations between neighboring pixels, edge and
texture information of the moving object are also represented because wavelet
coefficients are obtained after high-pass filtering. Due to this reason the new
tracking algorithm is more robust than ordinary mean-shift tracking using only
color information.
A new content based image retrieval (CBIR) system is also developed in this
thesis. The CBIR system is based on mean-shift analysis using a joint-pdf. In
this system, the user selects a window in an image or an entire image and queries
similar images stored in a database. The selected region is represented using a
joint-pdf estimated from image pixels and their wavelet transform coefficients.
The retrieval algorithm is more reliable compared to other CBIR systems using
only color information or only edge or texture information because the jointpdf
based approach represents both texture, edge and color information. The
proposed method is also computationally efficient compared to sliding-window based retrieval systems because the joint-pdfs are compared in non-overlapping
windows. Whenever there is a reasonable amount of match between the queried
window and the original image window then a mean-shift analysis is started.Cüce, Halil İbrahimM.S
Hausdorff-Distance Enhanced Matching of Scale Invariant Feature Transform Descriptors in Context of Image Querying
Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale Invariant Feature Transform (SIFT) descriptors have been matched using our presented algorithm, followed by the computation of our related similarity measure. This approach has shown excellent performance in both retrieval accuracy and speed
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
This paper addresses unsupervised discovery and localization of dominant
objects from a noisy image collection with multiple object classes. The setting
of this problem is fully unsupervised, without even image-level annotations or
any assumption of a single dominant class. This is far more general than
typical colocalization, cosegmentation, or weakly-supervised localization
tasks. We tackle the discovery and localization problem using a part-based
region matching approach: We use off-the-shelf region proposals to form a set
of candidate bounding boxes for objects and object parts. These regions are
efficiently matched across images using a probabilistic Hough transform that
evaluates the confidence for each candidate correspondence considering both
appearance and spatial consistency. Dominant objects are discovered and
localized by comparing the scores of candidate regions and selecting those that
stand out over other regions containing them. Extensive experimental
evaluations on standard benchmarks demonstrate that the proposed approach
significantly outperforms the current state of the art in colocalization, and
achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
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