Mitsubishi Electric Research Laboratories

About: Mitsubishi Electric Research Laboratories is a based out in . It is known for research contribution in the topics: Signal & Pixel. The organization has 1238 authors who have published 3800 publications receiving 131600 citations. The organization is also known as: MERL.

Image quilting for texture synthesis and transfer

Abstract: We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces surprisingly good results for a wide range of textures. Second, we extend the algorithm to perform texture transfer — rendering an object with a texture taken from a different object. More generally, we demonstrate how an image can be re-rendered in the style of a different image. The method works directly on the images and does not require 3D information.

Example-based super-resolution

Abstract: We call methods for achieving high-resolution enlargements of pixel-based images super-resolution algorithms. Many applications in graphics or image processing could benefit from such resolution independence, including image-based rendering (IBR), texture mapping, enlarging consumer photographs, and converting NTSC video content to high-definition television. We built on another training-based super-resolution algorithm and developed a faster and simpler algorithm for one-pass super-resolution. Our algorithm requires only a nearest-neighbor search in the training set for a vector derived from each patch of local image data. This one-pass super-resolution algorithm is a step toward achieving resolution independence in image-based representations. We don't expect perfect resolution independence-even the polygon representation doesn't have that-but increasing the resolution independence of pixel-based representations is an important task for IBR.

Detecting Pedestrians Using Patterns of Motion and Appearance

Abstract: This paper describes a pedestrian detection system that integratesimage intensity information with motion information.We use a detection style algorithm that scans a detectorover two consecutive frames of a video sequence. Thedetector is trained (using AdaBoost) to take advantage ofboth motion and appearance information to detect a walkingperson. Past approaches have built detectors based onmotion information or detectors based on appearance information,but ours is the first to combine both sources ofinformation in a single detector. The implementation describedruns at about 4 frames/second, detects pedestriansat very small scales (as small as 20x15 pixels), and has avery low false positive rate.Our approach builds on the detection work of Viola andJones. Novel contributions of this paper include: i) developmentof a representation of image motion which is extremelyefficient, and ii) implementation of a state of theart pedestrian detection system which operates on low resolutionimages under difficult conditions (such as rain andsnow).

Seam carving for content-aware image resizing

Abstract: Effective resizing of images should not only use geometric constraints, but consider the image content as well We present a simple image operator called seam carving that supports content-aware image resizing for both reduction and expansion A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function By repeatedly carving out or inserting seams in one direction we can change the aspect ratio of an image By applying these operators in both directions we can retarget the image to a new size The selection and order of seams protect the content of the image, as defined by the energy function Seam carving can also be used for image content enhancement and object removal We support various visual saliency measures for defining the energy of an image, and can also include user input to guide the process By storing the order of seams in an image we create multi-size images, that are able to continuously change in real time to fit a given size

Robust real-time object detection.

Abstract: 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 Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA