object detection

Rapid object detection using a boosted cascade of simple features
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This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image

Robust real-time object detection
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This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the Integral

A general framework for object detection
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This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an

Example-based object detection in images by components
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In this paper, we present a general example-based framework for detecting objects in static images by components. The technique is demonstrated by developing a system that locates people in cluttered scenes. The system is

Multiple instance boosting for object detection
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A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of

Real-time object detection for smart vehicles
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This paper presents an efficient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety of object shapes; efficient hierarchies can be

Statistical modeling of complex backgrounds for foreground object detection
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This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this

A statistical approach to 3D object detection applied to faces and cars
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In this thesis, we describe a statistical method for 3D object detection . In this method, we decompose the 3D geometry of each object into a small number of viewpoints. For each viewpoint, we construct a decision rule that determines if the object is present at that specific

Sharing features: efficient boosting procedures for multiclass object detection
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We consider the problem of detecting a large number of different object classes in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, which can be slow and require much training data. We present a multi-class boosting

Contextual models for object detection using boosted random fields
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We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF)

Object detection using a max-margin hough transform
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We present a discriminative Hough transform based object detector where each local part casts a weighted vote for the possible locations of the object center. We show that the weights can be learned in a max-margin framework which directly optimizes the

Coupled detection and trajectory estimation for multi- object tracking
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We present a novel approach for multi- object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. It is formulated in a hypothesis selection framework and builds upon a state-of-the-art pedestrian detector. At

Boosting Chain Learning for Object Detection .
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A general classification framework, called boosting chain, is proposed for learning boosting cascade. In this framework, a chain structure is introduced to integrate historical knowledge into successive boosting learning. Moreover, a linear optimization scheme is proposed to

Feature-centric evaluation for efficient cascaded object detection
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We describe a cascaded method for object detection . This approach uses a novel organization of the first cascade stage called feature-centric evaluation which re-uses feature evaluations across multiple candidate windows. We minimize the cost of this

Performance evaluation of object detection and tracking systems
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This paper presents a set of metrics and algorithms for performance evaluation of object tracking systems. Our emphasis is on wide-ranging, robust metrics which can be used for evaluation purposes without inducing any bias towards the evaluation results. The goal is to

A 3D time of flight camera for object detection
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The knowledge of three-dimensional data is essential for many control and navigation applications. Especially in the industrial and automotive environment a fast and reliable acquisition of 3D data has become a main requirement for future developments. Moreover

Binary partition trees for object detection
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(BPTs) for object detection . BPTs are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the

Learning a restricted Bayesian network for object detection
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Many classes of images have the characteristics of sparse structuring of statistical dependency and the presence of conditional independencies among various groups of variables. Such characteristics make it possible to construct a powerful classifier by only

Weakly supervised object detector learning with model drift detection
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A conventional approach to learning object detectors uses fully supervised learning techniques which assumes that a training image set with manual annotation of object bounding boxes are provided. The manual annotation of objects in large image sets is

A survey on object detection and tracking methods
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The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. The object detection and object classification are preceding steps for tracking an object in sequence of images. Object


  • Avantika

    can you suggest me new projects to work on image processing?