Background/purpose: Border (B) description of melanoma and other pigmented skin lesions is one of the most important tasks for the clinical diagnosis of dermoscopy images using the ABCD rule. For an accurate description of the border, there must be an effective skin tumor area extraction (STAE) method. However, this task is complicated due to uneven illumination, artifacts present in the lesions and smooth areas or fuzzy borders of the desired regions.



Methods: In this paper, a novel STAE algorithm based on improved dynamic programming (IDP) is presented. The STAE technique consists of the following four steps: color space transform, pre-processing, rough tumor area detection and refinement of the segmented area. The procedure is performed in the CIE L*a*b* color space, which is approximately uniform and is therefore related to dermatologist's perception. After pre-processing the skin lesions to reduce artifacts, the DP algorithm is improved by introducing a local cost function, which is based on color and texture weights.



Results: The STAE method is tested on a total of 100 dermoscopic images. In order to compare the performance of STAE with other state-of-the-art algorithms, various statistical measures based on dermatologist-drawn borders are utilized as a ground truth. The proposed method outperforms the others with a sensitivity of 96.64%, a specificity of 98.14% and an error probability of 5.23%.



Conclusion: The results demonstrate that this STAE method by IDP is an effective solution when compared with other state-of-the-art segmentation techniques. The proposed method can accurately extract tumor borders in dermoscopy images.

Skin tumor area extraction using an improved dynamic programming approach.

Pervasive networking and sophisticated computing open opportunities for collaborative information processing independent of time and space. In this instance the information system becomes an enhancer of human intellect, as well as a mediator for communication among participants. The human user favors the sensory dimensions of sight, sound, and touch as primary channels of communication. Machines that can accommodate these modes promise flexibilities and functionalities that transcend the traditional mouse and keyboard. The paper describes research to establish human-computer interfaces that capture attributes of natural face-to-face communication. An experimental multimodal system is developed to study several aspects of natural style human-computer communication. While as yet primitive, the technologies of image and gaze processing, hands-free conversation, and force feedback tactile transduction are combined and used simultaneously for manipulating objects in a shared workspace. Software agents fuse the sensory signals to estimate and interpret user intent. Current areas of experimental application include disaster relief/crisis management, telemedicine/rehabilitation, and mobile office/wearable computers.

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Natural communication with information systems

Can a room be intelligent? The article presents the thoughts and work of four people who not only believe the answer is yes, but are working towards making this happen. It discusses insights gleaned from work at the Intelligent Room project at the MIT AI Lab. In contrast to the two intelligent rooms built in the MIT AI Lab's quarters in an otherwise (comparatively) mundane office building in Cambridge, Massachusetts, it also considers experiences making an entire house in Colorado behave intelligently. The experiences of the chief systems architect for the Gates Estate, Bill Gates' well-publicized 66,000 square foot new home in Medina, Washington, are also discussed. Finally, some of the functionalities that we would want intelligent rooms with large numbers of people-such as lecture halls and conference venues-to possess, are presented.

Room service, AI-style

The ego-noise generated by the motors and propellers of a micro aerial vehicle (MAV) masks the environmental sounds and considerably degrades the quality of the on-board sound recording. Sound enhancement approaches generally require knowledge of the direction of arrival of the target sound sources, which are difficult to estimate due to the low signal-to-noise-ratio (SNR) caused by the ego-noise and the interferences between multiple sources. To address this problem, we propose a multi-modal analysis approach that jointly exploits audio and video to enhance the sounds of multiple targets captured from an MAV equipped with a microphone array and a video camera. We first address audio-visual calibration via camera resectioning, audio-visual temporal alignment and geometrical alignment to jointly use the features in the audio and video streams, which are independently generated. The spatial information from the video is used to assist sound enhancement by tracking multiple potential sound sources with a particle filter. Then we infer the directions of arrival of the target sources from the video tracking results and extract the sound from the desired direction with a time-frequency spatial filter, which suppresses the ego-noise by exploiting its time-frequency sparsity. Experimental demonstration results with real outdoor data verify the robustness of the proposed multi-modal approach for multiple speakers in extremely low-SNR scenarios.

Multi-Modal Localization and Enhancement of Multiple Sound Sources from a Micro Aerial Vehicle

this kind of activity. The two most important have proved to be logging and general object-state access. Logging. I can't argue strongly enough for our object-network OS to support a ubiquitous, low-impact logging facility. It's equally important that object implementations are fully instrumented with log output and that such instrumentation never be removed. Once a system is having a problem, it's too late to think about adding logging to the code. We have had many complicated and hard-to-reproduce problems what I'll call nth-order feedback classes of problems such as oscillation. Without the ability to snoop around and watch log activity, problems would be nearly impossible to diagnose. Using our project's implementation as an example, let me describe what I think are the key aspects of a suitable logging facility. First, it is very useful to classify log-record output hierarchically. Typically , there would be classes and subclasses for maintenance, error, warning, and trace kinds of output. From the programmer's perspective, the logging primitives should be right there as part of their programming model and runtime environment. We keep a store-and-forward log facility object on each machine, whose job is to very quickly buffer local log activity into a disk-based FIFO queue, then forward that queue's content asynchronously to another facility (typically a central store that can be monitored on the fly). In addition , we can turn on and off different levels of log output at the object-instance level. Typically, our system runs objects with all trace output turned off, but if we're tracking down a problem we can reach in while an object is instantiated and modify its output log filter. This approach has proven essential in our system. Without such logging facilities, a system even an order of magnitude smaller would be impossible to operate. General object-state access. The ability to probe the general state of an object instance while it's instantiated is also very useful. If we think about the general nature of the objects within our object-network OS, we see a high degree of common behavior. For example, any object can publish and subscribe to properties, listen for events, or reference and be referenced by other objects. In our implementation, this list of common traits is even longer. It would be very nice to be able to ask any object in the system to dump its current state. As with logging, this kind of facility is …

Autodirective sound capture: towards smarter conference rooms

A real-time face tracker is presented in this paper. The system has achieved 15 frames/second tracking using a Pentium 200 PC with a Datacube MaxPCI image processing board and a Panasonic RGB color camera. It tracks human faces in the camera's field of view while people move freely. A stochastic model to characterize the skin color distribution of human skin is used to segment the face and other skin areas from the background. Median filtering is then used to clean up the background noise. Geometric constraints are applied to the segmented image to extract the face from the background. To reduce computation and achieve real-time tracking, 1D projections (horizontal and vertical) of the image are analyzed instead of the 2D image. Run-length- encoding and frequency domain analysis algorithms are used to separate faces from other skin-like blobs. The system is robust to illumination intensity variations and different skin colors. It can be applied to many human-computer interaction applications such as sound locating, lip- reading, gaze tracking and face recognition.

Real-time face tracking

A visual information directed microphone array system is presented in this paper. This system uses a real-time mouth tracking system to direct a beam-former focusing on the mouth. The microphone array system is implemented on a PC with a Signalogic 8-channel DSP board and reports a better signal-to-noise ratio sound capturing in a high noise environment.

Visual-information-assisted microphone array processing in a high-noise environment

Color and texture have long been used as image features to segment and classify images. In most of the previous approaches, color and texture are used as two uncorrelated features, while in the real world, the spatial information and spectral information of an image are often tightly coupled. An feature extraction algorithm is studied in this paper, which represents colored texture in a unified way. With this approach, different spectral channels are correlated spatially to give an unified representation of both the color and texture information. In order to use this feature in image segmentation applications, properties of the feature are studied. A novel segmentation algorithm is proposed based on the study. Preliminary segmentation results are presented.

Color texture analysis: a unified approach

A machine vision system used for quantitative analysis of the uniformity of powder blending has been built at the CAIP center of Rutgers University. A wide variety of instruments are used in the system in order to perform the image processing algorithms required. This paper will introduce the system, focusing on the high speed image processing hardware configurations and discussing the scheme for developing the software to manage this comprehensive system.

High-performance image processing system for powder mixture analysis

A high frame rate display system has been developed to present image sequences at 114.4 frames/sec as opposed to the video frame rate of 30 frames/sec. In order to do this, we are using a video display monitor in stereo mode and a Datacube MV-200 image processing hardware system to display images at rates approximately twice as fast as would otherwise be possible. We propose to use this system to assess the elderly diver's ability to locate and distinguish computer generated images of vehicles and to determine their direction of motion in a variety of simulated conditions.

Yufeng Liang

Papers

Real-time face tracking

Visual-information-assisted microphone array processing in a high-noise environment

Color texture analysis: a unified approach

High-performance image processing system for powder mixture analysis

High-frame-rate display system for study of motion perception