Biomedical subjects

Jiebo Luo

Publications and source records attributed to Jiebo Luo.

5 recordsLinked to original sources

PubMed2006

Color object detection using spatial-color joint probability functions.

Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin.

Algorithms↗

PubMed2006

Robust online orientation correction for radiographs in PACS environments.

In picture archiving and communications systems (PACS), images need to be displayed in standardized ways for radiologists' interpretations. However, for most radiographs acquired via computed radiography (CR), digital radiography (DR), or digitized films, the image orientation is undetermined because of the variations in examination conditions and patients' situations. To address this problem, an automatic orientation correction method is developed. It first detects the most indicative region in a radiograph for image orientation, and then extracts a set of low-level visual features from the region. Based on these features, a well-trained classifier, using support vector machines, is employed to recognize the correct orientation of the radiograph and reorient it to the desired position. A large-scale experiment was conducted on more than 12 000 radiographs, which covered a wide variety of exam types, to validate the method. The overall success rate of orientation correction was 96.1%. A workflow study on the method also demonstrated a significant improvement in efficiency for image display. To our knowledge, this work represents the first robust system designed to handle all radiographic exam types using a unified framework instead of using dedicated strategies for different exam types.

Algorithms↗

PubMed2005

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

Algorithms↗

PubMed2005

Image transform bootstrapping and its applications to semantic scene classification.

The performance of an exemplar-based scene classification system depends largely on the size and quality of its set of training exemplars, which can be limited in practice. In addition, in nontrivial data sets, variations in scene content as well as distracting regions may exist in many testing images to prohibit good matches with the exemplars. Various boosting schemes have been proposed in machine learning, focusing on the feature space. We introduce the novel concept of image-transform bootstrapping using transforms in the image space to address such issues. In particular, three major schemes are described for exploiting this concept to augment training, testing, and both. We have successfully applied it to three applications of increasing difficulty: sunset detection, outdoor scene classification, and automatic image orientation detection. It is shown that appropriate transforms and meta-classification methods can be selected to boost performance according to the domain of the problem and the features/classifier used.

Algorithms↗

PubMed2003

Psychophysical study of image orientation perception.

The experiment reported here investigates the perception of orientation of color photographic images. A collection of 1000 images (mix of professional photos and consumer snapshots) was used in this study. Each image was examined by at least five observers and shown at varying resolutions. At each resolution, observers were asked to indicate the image orientation, the level of confidence, and the cues they used to make the decision. The results show that for typical images, accuracy is close to 98% when using all available semantic cues from high-resolution images, and 84% when using only low-level vision features and coarse semantics from thumbnails. The accuracy by human observers suggests an upper bound for the performance of an automatic system. In addition, the use of a large, carefully chosen image set that spans the 'photo space' (in terms of occasions and subject matter) and extensive interaction with the human observers reveals cues used by humans at various image resolutions: sky and people are the most useful and reliable among a number of important semantic cues.

Adult↗