Showing papers on "Object-class detection published in 1994"

Face locating and tracking for human-computer interaction

Abstract: Effective human-to-human communication involves both auditory and visual modalities, providing robustness and naturalness in realistic communication situations. Recent efforts at our lab are aimed at providing such multimodal capabilities for human-machine communication. Most of the visual modalities require a stable image of a speaker's face. We propose a connectionist face tracker that manipulates camera orientation and room, to keep a person's face located at all times. The system operates in real time and can adapt rapidly to different lighting conditions, cameras and faces, making it robust against environmental variability. Extensions and integration of the system with a multimodal interface are presented. >

Multiple object recognition and scene interpretation for autonomous road vehicle guidance

Abstract: The seeing car VaMoRs-P is the next step of development in computer vision for autonomous road vehicle guidance at the 'Universitat der Bundeswehr Munich' (UBM). As one of the key functions, the module 'obstacle detection and tracking (ODT)' has been developed since 1991; with several thousand kilometers of autonomous test driving a state of high reliability has been reached. For vehicles in the vehicle's own lane the well known extraction of the left and right object boundaries using fast contour analysis is performed. Additionally, a new approach for recognition of vehicles in the neighboring lanes and those approaching from the rear is being developed. The lower object boundary detection using a knowledge based edge chaining algorithm and a special edge detector is presented. VaMoRs-P is equipped with four miniature CCD-cameras with different focal lengths mounted on two platforms viewing to the front and rear of the own car. For obstacle detection a range of 6 up to 120 meters distance in the own and the neighboring left and right lane is achieved. For collision avoidance and autonomous overtaking of slower driving road users a minimum number of 4 or 5 objects must be tracked in parallel. For this task ODT comprises about 15 transputers for image processing and state estimation.

A change-detection method for multiple object localization in real scenes

Abstract: The aim of this paper is to show a real-time change detection method for multiple object localization from real world image sequences. Limits, quality and time performances of the described pixel-oriented method are outlined comparing it with other existing techniques. Results are presented by applying the the technique described in the architecture of a real-time surveillance system for visual control of an unattended level-crossing. The localization of detected objects is also addressed and tested on real scenes where illumination is not assumed to be constant. >

Detection and pose estimation of human face with synthesized image models

Abstract: This paper presents a new method for detecting a human face, and estimating its pose while tracking it in real image sequences. The virtue of the method is that parameterized qualitative features derived from a lot of sampled facial images are introduced in the detection process, and in the face tracking process, some temporary model images of the face with various poses are synthesized by a texture mapping technique and utilized. While tracking the detected face, many model images are accumulated and the pose of the human face is estimated as a linear combination of correlations between the models.

Morphological and wavelet transforms for object detection and image processing.

Abstract: We consider the problem of detecting multiple distorted objects in an input scene with clutter. The input scenes contain different types of background clutter and multiple objects in different classes, with different object aspect views, different object representations, hot/cold/bimodal/partial object variations, and high/low contrast object variations. Several new optical morphological operations for use in the above detection problem and in other general low-level image-processing applications are described, and several examples of their use are provided. For difficult detection problems in which high detection rates and low false-alarm rates are required we combine morphological operations and optical wavelet transforms to reduce clutter and improve object detection. The details of this set of filters and initial test results are given. The most computationally demanding operations required in all cases are realizable on an optical correlator.

Efficient collision detection among objects in arbitrary motion using multiple shape representations

Abstract: We propose an efficient method for detecting potential collisions among multiple objects with arbitrary motion (translation and rotation) in 3D space. The method is useful for online monitoring and path planning in a 3D environment in which there are multiple independently-moving objects. The method consists of two main stages: 1) the coarse stage, an approximate test is performed to identify interfering objects in the entire workspace using octree representation of object shapes; and 2) the fine stage, polyhedral representation of object shapes is used to more accurately identify any object parts that might cause interference and collisions. For this purpose, specific pairs of faces belonging to any of the interfering objects found in the first stage are tested, thus performing detailed computation on a reduced amount of data. Experimental results, which demonstrate the efficiency of the proposed collision detection method, are given.

Face image creation device and method using part images of the plural parts of the face image

Abstract: An object image creation device and method which combine images of the parts which compose an object into an object image. A storage stores data on a plurality of kinds of images for each of the parts which compose one half of an object. Any images are selected one from among a stored plurality of kinds of images of each of the parts. The selected images of the respective parts are combined into a first half object image. A second half object image symmetrical with the first half object image is produced on the basis of the first half object image. The first and second half object images are combined into a complete object image.

Tracking of tubular objects for scientific applications

Abstract: We present a system for detection, tracking and representation of tubular objects in images. The uniqueness of the proposed system is twofold: at the macro level, the novelty of the system lies in the integration of object localization and tracking using geometric properties; at the micro level, is the use of high and low level constraints to model the detection and tracking subsystem. The underlying philosophy for object detection is to extract perceptually significant features from the pixel level image, and then use these high level cues to refine the precise boundaries. In the case of tubular objects, the perceptually significant features are anti-parallel line segments or, equivalently, their axis of symmetries. The axis of symmetry infers a coarse description of the object in terms of a bounding polygon. The polygon then provides the necessary boundary condition for the refinement process, which is based on dynamic programming. For tracking the object in a time sequence of images, the refined contour is then projected onto each consecutive frame. In addition, the system provides an axis of symmetry representation of object for subsequent scientific analysis. >

Multilevel hit-miss transform for object detection

Abstract: The rank-order hit-miss transform (HMT) filter is a significant new advancement in pattern recognition. We detail a new version of our HMT algorithm used for detection, the filter parameters used, and detection (PD) and false alarm (PFA) results. In detection, these filters are required to locate all objects in a scene with clutter present. This must be achieved for objects in multiple different classes, with 3-D distortion and contrast differences present. Thus, they represent considerably new image processing filters.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image screening based on projection pursuit for image recognition

Abstract: A new preprocessing method, called image screening, is presented for improving the recognition rate and efficiency in statistical image recognition. The problem of detecting a specified object in an input image is treated as a two-class classification problem in which the image falls into a set of subimages in the target object (figure) class and the other set of subimages in the ground class. An image screening algorithm selects a candidate set of subimages which are similar to the object class and rejects the remaining set using screening filters whose design is based on projection pursuit. The classifiers of the two classes are taken from the candidate set. Experimental results for eye and mouth detection in face images and text-area detection in document images demonstrate that this method improves the recognition rate and efficiency.