This paper proposes two algorithms to balance energy consumption among sensor nodes by distributing the workload of image compression tasks within a cluster on wireless sensor networks. The main point of the proposed algorithms is to adopt the energy threshold, which is used when we implement the exchange and/or assignment of tasks among sensor nodes. The threshold is well adaptive to the residual energy of sensor nodes, input image, compressed output, and network parameters. We apply the lapped transform technique, an extended version of the discrete cosine transform, and run length encoding before Lempel-Ziv-Welch coding to the proposed algorithms to improve both quality and compression rate in image compression scheme. We extensively conduct computational experiments to verify the our methods and find that the proposed algorithms achieve not only balancing the total energy consumption among sensor nodes and, thus, increasing the overall network lifetime, but also reducing block noise in image compression.

In this paper, a dual level access scheme is proposed to provide two levels of access to the broadcast data; one to video signals protected for authorized users, another to extra information e.g. advertisements provided for the remaining users in the network. In the scheme, video signals in MPEG format are considered. The video contents are protected from unauthorized viewing by encrypting the DC coefficients of the luminance component in I-frames, which are extracted from the MPEG bit-stream. An improved direct sequence spread spectrum technique is used to add extra information to non-zero AC coefficients, extracted from the same MPEG bit-stream. The resultant MPEG bit-stream still occupies the same existing bandwidth allocated for a broadcast channel. At the receiver, the extra information is recovered and subtracted from the altered AC coefficients. The result is then combined with the decrypted DC coefficients to restore the original MPEG bit-stream. The experimental results show that less than 2.9% of the size of MPEG bit-stream was required to be encrypted in order to efficiently reduce its commercial value. Also, on average, with a 1.125 Mbps MPEG bit-stream, an amount of extra information up to 1.4 kbps could be successfully transmitted, while the video quality (PSNR) was unnoticeably degraded by 2.81 dB.

This paper proposes a human interface where a novel input method is used to substitute conventional input devices. It overcomes the deficiencies of physical devices, as it is based on image processing techniques. The proposed interface is composed of three parts: extraction of a person's handshape from a digitized image, detection of its fingertip, and interpretation by a software application. First, images of a pointing hand are digitized to obtain a sequence of monochrome frames. In each frame the hand is isolated from the background by means of gray-level slicing; with threshold values calculated dynamically by the combination of movement detection and histogram analysis. The advantage of this approach is that the system adapts itself to any user and compensates any changes in the illumination, while in conventional methods the threshold values are previously defined or markers have to be attached to the hand in order to give reference points. Second, once the hand is isolated, fingertip coordinates are extracted by scanning the image. Third, the coordinates are inputted to an application interface. Overall, as the algorithms are simple and only monochrome images are used, the amount of processing is kept low, making this system suitable to real-time processing without needing expensive hardware.

The Institute for Posts and Telecommunications Policy (IPTP) held its first character recognition competition in 1992 to ascertain the present status of ongoing research in character recognition and to find promising algorithms for handwritten numerals. In this paper, we report and analyze the results of this competition. In the competition, we adopted 3-digit handwritten postal code images gathered from live mail as recognition objects. Prior to the competition, 2,500 samples (7,500 characters) were distributed to the participants as traning data. By using about 10,000 different samples (29,883 characters), we tested 13 recognition programs submitted by five universities and eight manufacturing companies. According to the four kinds of evaluation criteria: recognition accuracy, recognition speed, robustness against degradation, and theroretical originality, we selected the best three recognition algorithms as the Prize of Highest Excellence. Interestingly enough, the best three recognition algorithms showed considerable diversity in their methodologies and had very few commonly substituted or rejected patterns. We analyzed the causes for these commonly substituted or rejected patterns and, moreover, examined the human ability to discriminate between these patterns. Next, by considering the complementary characteristics of each recognition algorithm, we studied a multi-expert recognition strategy using the best three recognition algorithms. Three kinds of combination rules: voting on the first candidate rule, minimal sum of candidate order rule, and minimal sum of dissimilarities rule were examined, and the latter two rules decreased the substitution rate to one third of that obtained by one-expert in the competition. Furthermore, we proposed a candidate appearance likelihood method which utilizes the conditional probability of each of ten digits given the candidate combination obtained by each algorithm. From the experiments, this method achieved surprisingly low values of both substitution and rejection rates. By taking account of its learning ability, the candidate appearance likelihood method is considered one of the most promising multi-expert systems.

We apply neural networks to implement a line shape recognition/classification system. The purpose of employing neural networks is to eliminate target-specific algorithms from the system and to simplify the system. The system needs only to be trained by samples. The shapes are captured by the following operations. Lines to be processed are segmented at inflection points. Each segment is extended from both ends of it in a certain percentage. The shape of each extended segment is captured as an approximate curvature. Curvature sequence is normalized by size in order to get a scale-invariant measure. Feeding this normalized curvature date to a neural network leads to position-, rotation-, and scale-invariant line shape recognition. According to our experiments, almost 100% recognition rates are achieved against 5% random modification and 50%-200% scaling. The experimental results show that our method is effective. In addition, since this method captures shape locally, partial lines (caused by overlapping etc.) can also be recognized.

It is known that the resolution conversion based on orthogonal transform has a problem that is difference of luminance between the converted image and the original. In this paper, the scale factor of the system employing various orthogonal transforms is generally formulated by considering the DC gain, and the condition of alias free for DC component is indicated. If the condition is satisfied, then the scale factor is determined by only the basis functions.