Abstract: A method for analyzing images to generate a plurality of output features includes receiving input features of the image and performing Fourier transforms on each input feature. Kernels having coefficients of a plurality of trained features are received and on-the-fly Fourier transforms (OTF-FTs) are performed on the coefficients in the kernels. The output of each Fourier transform and each OTF-FT are multiplied together to generate a plurality of products and each of the products are added to produce one sum for each output feature. Two-dimensional inverse Fourier transforms are performed on each sum.

Abstract: A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.

Abstract: A confidence map for optical flow gradients is constructed calculating a set of census texture gradients for each pixel of an image, filtering said gradients and extracting confidence values from said gradients using a plurality of decision tree classifiers. A confidence map is then generated from said confidence values.

Abstract: A confidence map for optical flow gradients is constructed calculating a set of census texture gradients for each pixel of an image, filtering said gradients and extracting confidence values from said gradients using a plurality of decision tree classifiers. A confidence map is then generated from said confidence values.

Abstract: Estimation of the ground plane of a three dimensional (3D) point cloud based modifications to the random sample consensus (RANSAC) algorithm is provided. The modifications may include applying roll and pitch constraints to the selection of random planes in the 3D point cloud, using a cost function based on the number of inliers in the random plane and the number of 3D points below the random plane in the 3D point cloud, and computing a distance threshold for the 3D point cloud that is used in determining whether or not a 3D point in the 3D point cloud is an inlier of a random plane.

Abstract: Advanced driver assistance systems need to be able to recognize and to classify traffic signs under real time constraints, and under a wide variety of visual conditions. The invention shown employs binary masks extracted by color space segmentation, with a different binary mask generated for each sign shape. Temporal tracking is employed to add robustness to the detection system. The system is generic, and is trainable to the signs used in various countries.

Abstract: A vehicular structure from motion (SfM) system can store a number of image frames acquired from a vehicle-mounted camera in a frame stack according to a frame stack update logic. The SfM system can detect feature points, generate flow tracks, and compute depth values based on the image frames, the depth values to aid control of the vehicle. The frame stack update logic can select a frame to discard from the stack when a new frame is added to the stack, and can be changed from a first in, first out (FIFO) logic to last in, first out (LIFO) logic upon a determination that the vehicle is stationary. An optical flow tracks logic can also be modified based on the determination. The determination can be made based on a dual threshold comparison to insure robust SfM system performance.

Abstract: Methods and systems providing for real time structure from motion (SfM) processing in a computer vision system receiving images from a monocular camera are disclosed. The real time SfM processing described exploits constraints of the three dimensional (3D) environment that can be assumed for automotive applications and other applications with similar constraints.

Abstract: An image processing system includes a processor and optical flow (OF) determination logic for quantifying relative motion of a feature present in a first frame of video and a second frame of video that provide at least one of temporally and spatially ordered images with respect to the two frames of video. The OF determination logic configures the processor to implement performing OF estimation between the first frame and second frame using a pyramidal block matching (PBM) method to generate an initial optical flow (OF) estimate at a base pyramid level having integer pixel resolution, and refining the initial OF estimate using at least one pass of a modified Lucas-Kanade (LK) method to provide a revised OF estimate having fractional pixel resolution.

Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.

Abstract: A system and method for decoding video encoded using scalable video coding. In one embodiment, a decoder for scalable video coding (SVC) includes an SVC access unit analyzer and decoding logic. The SVC access unit analyzer is configured to examine an SVC access unit prior to layered decoding of the access unit, to determine, based on the examination, what operations the access unit specifies for each layer of the decoding, and to determine, based on the determined operations to be performed for each layer of the decoding, what data to store for use by a subsequent layer of the decoding. The decoding logic is configured to decode the access unit via a plurality of decoding layers; and to store at each decoding layer, for use by a subsequent decoding layer, the data determined by the SVC access unit analyzer to be used by the subsequent decoding layer.

Abstract: This invention is a method of scalable video encoding and decoding. The scalable video encoding codes both a base layer and an enhanced layer having greater resolution and/or refresh rate. Upon decoding some enhanced layer pictures may be dropped to reach a best resolution and refresh rate within a target data rate. Upon encoding a key picture in at least one group of pictures forming the video is a combined base layer/enhanced layer key picture. Such a combined base layer/enhanced layer key picture cannot be dropped on decoding. This technique reduces drift in the decoder.

Abstract: This invention predicts that intra mode prediction is more effective for the macro blocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.

Abstract: Disclosed examples include image processing methods and systems to process image data, including computing a plurality of scaled images according to input image data for a current image frame, computing feature vectors for locations of the individual scaled images, classifying the feature vectors to determine sets of detection windows, and grouping detection windows to identify objects in the current frame, where the grouping includes determining first clusters of the detection windows using non-maxima suppression grouping processing, determining positions and scores of second clusters using mean shift clustering according to the first clusters, and determining final clusters representing identified objects in the current image frame using non-maxima suppression grouping of the second clusters.

Abstract: An object detection system and a method of detecting an object in an image are disclosed. In an embodiment, a method for detecting the object includes computing one or more feature planes of one or more types for each image pixel of the image. A plurality of cells is defined in the image, where each cell includes first through nth number of pixels, and starting locations of each cell in the image in horizontal and vertical directions are integral multiples of predefined horizontal and vertical step sizes, respectively. One or more feature plane summations of one or more types are computed for each cell. A feature vector is determined for an image portion of the image based on a set of feature plane summations, and the feature vector is compared with a corresponding object classifier to detect a presence of the corresponding object in the image portion of the image.

Abstract: A confidence map for optical flow gradients is constructed calculating a set of gradients for each pixel of an image, filtering said gradients and extracting confidence values from said gradients using a plurality of decision tree classifiers. A confidence map is then generated from said confidence values.

Abstract: A video system includes an encoder for generating a compressed bit stream in response to a received video signal. The encoder includes a mode decision processor that is arranged to determine whether the mode of a first pixel block in a first row is an “intra-mode” or an “inter-mode.” The encoder also includes a mode estimation processor that is arranged to estimate the mode of a left pixel block in a second row that is received after the first row in response to the determined mode of the first pixel block in the first row. The encoder also includes a pixel block processor that is arranged to process a pixel block in the second row that is to the right of the left pixel block in response to the estimated mode of the left pixel block.

Abstract: An object detection system and a method of detecting an object in an image are disclosed. In an embodiment, a method for detecting the object includes computing one or more feature planes of one or more types for each image pixel of the image. A plurality of cells is defined in the image, where each cell includes first through nth number of pixels, and starting locations of each cell in the image in horizontal and vertical directions are integral multiples of predefined horizontal and vertical step sizes, respectively. One or more feature plane summations of one or more types are computed for each cell. A feature vector is determined for an image portion of the image based on a set of feature plane summations, and the feature vector is compared with a corresponding object classifier to detect a presence of the corresponding object in the image portion of the image.

Abstract: A video encoder receives a macro-block of an image frame, and determines whether the macro-block contains text. The video encoder computes a quantization parameter for quantizing the macro-block, with the quantization parameter computed to be smaller if the macro-block is determined to contain text. The video encoder encodes the macro-block using the quantization parameter. Text quality in the encoded macro-block is preserved.

Abstract: A system and method for decoding video encoded using scalable video coding. In one embodiment, a decoder for scalable video coding (SVC) includes an SVC access unit analyzer and decoding logic. The SVC access unit analyzer is configured to examine an SVC access unit prior to layered decoding of the access unit, to determine, based on the examination, what operations the access unit specifies for each layer of the decoding, and to determine, based on the determined operations to be performed for each layer of the decoding, what data to store for use by a subsequent layer of the decoding. The decoding logic is configured to decode the access unit via a plurality of decoding layers; and to store at each decoding layer, for use by a subsequent decoding layer, the data determined by the SVC access unit analyzer to be used by the subsequent decoding layer.