Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc. While digitization of documents such as scientific articles and magazines is prevalent in literature, one of the main challenges for digitization of newspaper lies in its complex layout (e.g. articles spanning multiple columns, text interrupted by images) analysis, which is necessary to preserve human read-order. This work provides a major breakthrough in the digitization of newspapers on three fronts: first, releasing a dataset of 3000 fully-annotated, real-world newspaper images from 21 different U.S. states representing an extensive variety of complex layouts for document layout analysis; second, proposing layout segmentation as a precursor to existing optical character recognition (OCR) engines, where multiple state-of-the-art image segmentation models and several post-processing methods are explored for document layout segmentation; third, providing a thorough and structured evaluation protocol for isolated layout segmentation and end-to-end OCR.

Many types of epileptic seizures are accompanied by characteristic eye movement, along with the movement of other parts of the body. Vision-based detection of eye movement in the context of seizures is less explored. The contribution of this paper is three-fold: (1) a compute-efficient algorithm for eyeball detection is proposed and evaluated on a standard database and yields an accuracy of 95%. (2) A new video dataset for absence seizure has been created. (3) The proposed algorithm for eyeball detection is applied on this dataset in order to detect the occurrence of absence seizure.

We propose a method to automatically detect emotions in unconstrained settings as part of the 2013 Emotion Recognition in the Wild Challenge [16], organized in conjunction with the ACM International Conference on Multimodal Interaction (ICMI 2013). Our method combines multiple visual descriptors with paralinguistic audio features for multimodal classification of video clips. Extracted features are combined using Multiple Kernel Learning and the clips are classified using an SVM into one of the seven emotion categories: Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. The proposed method achieves competitive results, with an accuracy gain of approximately 10% above the challenge baseline.

In this paper, we propose a novel method to robustly identify and classify arrow markings in road images. In the proposed method, simple and unique signatures are first derived for the various arrow types, based on signed edge maps and decomposing the arrows into smaller parts. The signed edge maps are processed using Hough Transform (HT), and the resulting Hough spaces are analyzed systematically, using a set of simple rules. The signatures are rotation-invariant and scale-invariant, thereby making the approach robust to variations in the appearance of the arrow markings. It is shown that the method yields a high detection and classification accuracy, of as high as 97% in the test images considered.

In this paper, we propose novel block-based techniques for robust extraction of lane marking edges in complex scenarios, such as in the presence of shadows, vehicles, other road markings etc. The techniques are based on the properties of lane markings and involve a two-stage processing: (1) generation of customized edge maps using histograms of gradient angles, and (2) directional signed edges in combination with Hough Transform to identify lane markings. It is shown that the proposed techniques show a detection accuracy of as high as 98% on test data collected on real road scenarios, representing the various complex cases.

Non-collinear edge pixels are equivalent to noise for the linear Hough transform (LHT). Existing methods that reduce the number of points for Hough voting are based on random and/or probabilistic selection. Such methods select both collinear and noisy pixels, thereby incurring unwanted computational costs. In this paper, we propose a novel gradient angle histogram based technique to generate modified straight line edge map (SLEM), which largely retains the straight line edges and eliminates noisy edge pixels. A block-based SLEM generation is proposed to increase the robustness of straight line extraction and validated on test images. Further, effect of varying block sizes on accuracy of straight line detection is studied and appropriate block settings are derived. The proposed gradient angle histogram based method reduces the number of edge pixels by as much as 85%.

Real-time image rotation is a necessary task in many vision based applications. Most available image rotation architectures are serial in nature, while the existing parallel techniques suffer from computational bottlenecks, limiting their performance. In this paper, we propose techniques that not only speed up the rotation process but also reduce area, yielding an area-time efficient architecture. They exploit properties of symmetry in image coordinates for parallel image rotation. We show that the number of computations, the total computation time and area cost of the proposed architecture are lesser by as much as 80%, 55% and 67% respectively when compared to the most efficient parallel architecture in recent literature.

Accelerating Hough transform in hardware has been of interest due its popularity in real-time capable image processing applications. In most existing linear Hough transform architectures, an m xm edge map is serially read for processing, resulting in a total computation time of at least m(2) cycles. In this paper, we propose a novel parallel Hough transform computation method called the Additive Hough transform (AHT), wherein the image is divided using a k x k grid to reduce the total computation time by a factor of k(2). We have also proposed an efficient implementation of the AHT consisting of a look-up table (LUT) and two-operand adder arrays for every angle. Techniques to condense the LUT size have also been proposed to further reduce area utilization by as much as 50%. Our investigations based on employing an 8 x 8 grid shows a 1000 x speedup compared to existing architectures for a range of image sizes. Area-time trade-off analysis has been presented to demonstrate that the area-time product of the proposed AHT-based implementation is at least 43% lower than other implementations reported in the literature. We have also included and characterized a hierarchical addition step in order to generate a global accumulation space equivalent to that of the conventional HT. It is shown that the proposed implementation with the hierarchical addition step remains superior to other methods in terms of both performance and area-time product metrics. Finally, we show that the proposed solution is equally efficient when applied on rectangular images.

The Hough transform, a widely used operation in image processing, suffers from being computationally intensive. In this letter, we propose a method called additive Hough transform (AHT) to accelerate Hough transform computation. It is based on parallel processing of k2points, obtained by dividing the edge map into uniform blocks using a k times k grid. It has been shown that AHT on verification gives the same Hough profile as the conventional Hough transform. Further, AHT reduces the total computation time by at least k2times as compared to existing Hough transform architectures.

Image preprocessing is an important step in most real-time neural-network based pattern recognition systems. Thumbnail generation is a commonly used preprocessing method to reduce the hardware complexity of the input layer of a neural network. A direct implementation of the averaging method to create thumbnails incurs high hardware cost. In this paper we propose an optimized averaging method that outperforms standard averaging method in terms of device utilization, maximum operating frequency and latency, when implemented using FPGA design methodology. It eliminates the use of accumulators and dividers and offers hardware savings of as much as 96% over standard averaging method. Also, the proposed averaging is performed in a single cycle making it more suitable for real-time implementations