Robert P. Loce

Facilitation of optimal binary morphological filter design via structuring element libraries and design constraints

For both the binary and gray scales, mean square optimal digital morphological filters have been characterized previously in terms of the Matheron erosion representation for increasing, translation-invariant mappings. Included in the characterization is the minimal search strategy for the optimal filter basis; however, without prior statistical information or an adequate image-noise model, even in the binary setting, filter design is computationally intractable for moderately sized observation windows. The mitigation of the computational burden via design constraints is the focus here. Although the resulting filter will be suboptimal, if the constraints are imposed in a suitable manner, little loss of filter performance occurs in return for design tractability. Three approaches are considered: limiting the number of terms in the Matheron expansion, constraining the observation window, and employing structuring element libraries. In the latter methodology various sublibraries are formed and a suboptimal filter is derived from image-noise statistics in conjunction with a basis search restricted to relevant sublibraries. This study analyzes two techniques for library construction: the expert approach involves prior sublibrary formation based on knowledge of important filter bases and the first-order approach employs single-erosion statistical information to limit the basis search to likely useful candidates.

Optimal mean-absolute-error hit-or-miss filters: morphological representation and estimation of the binary conditional expectation

The hit-or-miss operator is used as the building block of optimal binary restoration filters. Filter design methodologies are given for general-, maximum-, and minimum-noise environments, the latter two producing optimal thinning and thickening filters, respectively, and for iterative filters. The approach is based on the expression of translation-invariant filters as unions of hit-or-miss transforms. Unions of hit-or-miss transforms are expressed as canonical logical sums of products, and the final hit-or-miss templates are obtained by logic reduction. The net effect is a morphological representation and estimation of the conditional expectation, which is the overall optimal mean-absolute-error filter.

Precision of morphological-representation estimators for translation-invariant binary filters: increasing and nonincreasing

Abstract Mean-absolute-error-optimal, finite-observation, translations, invariant, binary-image filters have previously been characterized in terms of morphological representations: increasing filters as unions of erosions and nonincreasing filters as unions of hit-or-miss operators. Based on these characterizations, (sub)optimal filters have been designed via image-process realizations. The present paper considers the precision of filter estimation via realizations. The following problems are considered: loss of performance owing to employing erosion filters limited by basis size, precision in the estimation of erosion bases, and precision in the estimation of union-of-hit-or-miss filters. A key point: while precision deteriorates for both erosion and hit-or-miss filters as window size increases, the number of image realizations required to obtain good estimation in erosion-filter design can be much less than the number required for hit-or-miss-filter design. Thus, while in theory optimal hit-or-miss filtering is better because the unconstrained optimal hit-or-miss filter is the conditional expectation, owing to estimation error it is very possible that estimated optimal erosion filters are better than estimated optimal hit-or-miss filters.

Video-based real-time on-street parking occupancy detection system

Abstract. Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5  frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

Optimal binary differencing filters: design, logic complexity, precision analysis, and application to digital document processing

For given binary image and degradation processes, an optimal mean-absolute-error translation-invariant filter can be designed via the representation of such filters as a union of morphological hit-or-miss transforms. The present paper investigates a different optimization methodology by representing translation-invariant filters as differencing filters. Rather than employing structuring templates to build the entire output image, as is done with direct hit-or-miss representation, differencing filters only employ templates that locate value flips (black-to-white or white-to-black). Differencing filters play a central role in several digital document processing tasks and the paper considers their optimal design. The paper compares the logic designs of differencing and direct hit-or-miss representations, the combinational logic costs of the two representations, and the estimation precision of optimization approaches based on each. Both combinational logic cost and precision are relative to image models. It is also shown how differencing filters are statistically designed and applied in the digital document setting for image restoration and resolution conversion.

Modeling vibration-induced halftone banding in a xerographic laser printer

In a raster scanning printer, a laser beam is scanned across a photoreceptor in a direction perpendicular to the photoreceptor motion. When there is vibratory motion of the photoreceptor or wobble in the polygon mirror, the raster lines on the photoreceptor will not be evenly spaced. We analyze the positioning error and show that fractional raster spacing error is equal to photoreceptor fractional velocity error. These raster position errors can result in various print defects, of which halftone banding is the dominant defect. The dependences of halftone banding are examined using a first-order geometry-based printing model, an exposure model, and a more sophisticated laser imaging model coupled with a xerography model. The system model is used to calculate print reflectance modulation due to vibrations in both charged-area and discharged-area development modes using insulative or conductive development. System parameters examined are halftone frequency, raster frequency, average reflectance, vibration frequency, and multiple-beam interlace spacing.

Computer vision in roadway transportation systems: a survey

Abstract. There is a worldwide effort to apply 21st century intelligence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emission reduction. Computer vision is playing a key role in this transportation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges including imaging under a variety of environmental and illumination conditions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art.

Mean-absolute-error representation and optimization of computational-morphological filters

Abstract Computational mathematical morphology provides a framework for analysis and representation of range-preserving, finite-range operators in the context of mathematical morphology. As such, it provides a framework for statistically optimal design in the framework of a Matheron-type representation; that is, each increasing, translation-invariant filter can be expressed via the erosions generated by structuring elements in a basis. The present paper develops the corresponding mean-absolute-error representation, This representation expresses the error of estimation of a filter composed of some number of erosions in terms of single-erosion filter errors. There is a recursive form of the representation that permits calculation of filter errors from errors for filters composed of fewer structuring elements, Finally, the error representation is employed in designing an optimal filter to solve an image enhancement problem in electronic printing, the transformation of a 1-bit per pixel image into a 2-bit per pixel image.

Using structuring-element libraries to design suboptimal morphological filters

For both the binary and gray scales, mean-square optimal digital morphological filters have been fully characterized previously in terms of the Matheron erosion representation for increasing, translation-invariant mappings. Included in the characterization is the minimal search strategy for the optimal filter basis; nonetheless, in the absence of prior statistical information or an adequate image-noise model, even in the binary setting design is computationally intractable for moderately sized observation windows.The computational burden can be mitigated by imposing constraints on the filter. The present paper is mainly concerned with the development of structuring-element libraries to which the basis search can be confined. More specifically, the authors focus on the expert-library approach: various sublibraries are formed, each of which corresponds to certain key filters, the overall library is formed as the union of the sublibraries, and a suboptimal filter is derived from image-noise statistics in conjunction with a basis search restricted to relevant sublibraries.

Driver Cell Phone Usage Detection from HOV/HOT NIR Images

Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicles front windshield. The developed method consists of two stages, first, we localize the drivers face region within the front windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.