Mohammad T. Rahman

Real-time face-priority auto focus for digital and cell-phone cameras

Auto-focus (AF) has been a key feature in consumer level digital and cell-phone cameras allowing users to focus automatically on a particular plane in a scene in order to get a sharp image. Face priority AF has become of interest lately due to the fact that most pictures captured by consumers are of human faces. In face-priority AF, the focusing decision is made based on a detected face area in the image, thus capturing a sharp picture of the face. While many face detection algorithms exist in the literature, very few of them are actually suitable for real-time software deployment on resource limited digital or cell-phone camera processors. In this paper, a fast face-detection algorithm is introduced by combining a skin color model cluster with a computationally efficient shape processing scheme. Comparison results with a standard algorithm in terms of robustness, speed and accuracy are provided. This face detection algorithm is incorporated into our previously developed rule-based AF method to achieve real-time face-priority AF on an actual digital camera platform.

A new auto-focus sharpness function for digital and smart-phone cameras

Passive auto-focusing is a key feature in consumer level digital and smart-phone cameras and is used to capture focused images without any user intervention. This paper introduces a new sharpness function for achieving passive auto-focusing, where the image sharpness information is used to bring it into focus. A comparison is made between this introduced sharpness function and the commonly used sharpness functions in terms of accuracy and computation time. The results obtained indicate that the introduced sharpness function possesses a comparable accuracy while demanding less computation time.

Power reduction via near-optimal library-based cell-size selection

Assuming continuous cell sizes we have robustly achieved global minimization of the total transistor sizes needed to achieve a delay goal, thus minimizing dynamic power (and reducing leakage power). We then developed a feasible branch-and-bound algorithm that maps the continuous sizes to the discrete sizes available in the standard cell library. Results show that a typical library gives results close to the optimal continuous size results. After using state-of-the-art commercial synthesis, the application of our discrete size selection tool results in a dynamic power reduction of 40% (on average) for large industrial designs.

Real-time implementation of robust face detection on mobile platforms

Although many face detection algorithms have been introduced in the literature, only a handful of them can meet the real-time constraints of mobile devices. This paper presents the real-time implementation of our previously introduced face detection algorithm on a mobile device. The steps taken to achieve such a real-time implementation are discussed. Real-time comparison results with the widely used Viola-Jones face detection algorithm in terms of detection rate and processing speed are presented to demonstrate the robustness of our real-time solution.

Real-time Face-based Auto-Focus for Digital Still and Cell-Phone Cameras

Auto-focus (AF) is a common feature in consumer level digital and cell-phone cameras. Face-based AF, or AF based on face detection, has become of interest due to the fact that the majority of pictures captured by consumers are of human faces. While many face detection algorithms exist in the literature, very few of them are actually suitable for real-time deployment on resource limited digital or cell-phone camera processors. In this paper, a face-detection algorithm combining a Gaussian skin color model with a computationally efficient sub-block postprocessing scheme is introduced to address the realtime constraints encountered in digital and cellphone cameras. This approach has been implemented in conjunction with our previously developed rule- based AF method in order to achieve real-time faced- based AF on the Texas Instruments programmable TMS320DM350 digital camera processor.

Post-synthesis leakage power minimization

We developed a new post-synthesis algorithm that minimizes leakage power while strictly preserving the delay constraint. A key aspect of the approach is a new threshold voltage (VT) assignment algorithm that employs a cost function that is globally aware of the entire circuit. Thresholds are first raised as much as possible subject to the delay constraint. To further reduce leakage, the delay constraint is then iteratively increased by Δ time units, each time enabling additional cells to have their threshold voltages increased. For each of the iterations, near-optimal cell size selection is applied so as to reacquire the original delay target. The leakage power iteratively reduces to a minimum, and then increases as substantial cell upsizing is required to re-establish the original delay target. We show results for benchmark and commercial circuits using a 40nm cell library in which four threshold voltage options are available. We show that the application of the new leakage power minimization algorithm appreciably reduces leakage power after multi-VT synthesis by a leading commercial tool, achieving an average post-synthesis leakage reduction of 37% while also reducing total active area and maintaining the original delay target.

Power reduction via separate synthesis and physical libraries

We introduce the concept of utilizing two cell libraries, one for synthesis and another for physical design. The physical library consists of only 9 functions, each with several drive and beta ratio options, for a total cell count of 186. We show that synthesis performs better with the inclusion of more complex cells (but only if they are power efficient), we augment the synthesis library to include numerous combinations of the basic 9 functions. The resulting synthesis library consists of a total of 865 cells. Note that these compound cells require only characterization (for a set of drive strengths, but only one beta ratio) and no layout. After design synthesis the compound cells are decomposed back to the basic (9) cells in the physical library. Then cell-size optimization is performed. The entire flow is efficient, with an ability to handle multi-million-gate commercial designs. Applied after state-of-the-art commercial synthesis, the application of a discrete cell-size selection tool, combined with the new dual library approach, results in a typical active area reduction of 40% for large current industrial designs, for the same delay.

Fast computation methods for estimation of image spatial entropy

Computation of image spatial entropy (ISE) is prohibitive in many applications of image processing due to its high computational complexity. Four fast or computationally efficient methods for estimation of ISE are thus introduced in this paper. Three of these estimation methods are parametric and the fourth one is non-parametric. The reduction in the computational complexity from the original formulation of ISE is made possible by making use of the Markovianity constraint which causes the joint histograms of neighboring pixels to become dense around their main diagonal. It is shown that by tolerating merely 1% estimation error, the order of complexity is significantly reduced and for applications that can tolerate 6% estimation error, the complexity is reduced to that of the classical monkey model entropy.

Power efficient standard cell library design

We propose a methodology to determine the contents of a power efficient library: a set of sizes (drives) and beta ratios (pMOS widths divided by nMOS widths) that will enable a designer to achieve the best power versus delay tradeoff. The methodology utilizes an optimum continuous gate sizing tool. The software is not only able to produce the optimum continuous power-delay trade-off curve but also perform near optimum discrete gate selection from a given point on the continuous curve. Our results suggest that size options 0.5X, 1X, 2X, 3X, 4X and 3–4 beta ratios centered on the optimum delay beta is the least complex library than can generate power efficient designs. The reduced library yields a performance loss less than 1.5% compared to a much larger library with finer granularity in sizes and betas.

Using image entropy maximum for auto exposure

To achieve auto exposure in digital cameras, image brightness is widely used because of its direct relationship with exposure value. To use image entropy as an alternative statistic to image brightness, it is required to establish how image entropy changes as exposure value is varied. This paper presents a mathematical proof along with experimental verification results to show that image entropy reaches a maximum value as exposure value is varied by changing shutter speed or aperture size.