A Lossless Image Compression using Modified Entropy Coding

Due to size limitation and complexity of the hardware in transmission applications, multimedia systems and computer communications, compression techniques are much necessary. The reasons for multimedia systems to compress the data, large storage is required to save the compressed data, the storage devices are relatively slow which in real-time, has constrain to play multimedia data, and the network bandwidth, that has limitations to real-time data transmission. This paper presents an enhanced approach of run length coding. First the DCT applied, and the quantization done on the image to be compressed, then the modified run length coding technique has been used to compress the image losslessly. This scheme represents the occurrence of repeated zeros by RUN, and a non-zero coefficient by LEVEL. It removes the value of RUN, as for the sequence of non-zero coefficients it is zero for most of the time and for a zero present between non-zero coefficients is replaced by ‘0’ which results in larger compression than RUN, LEVEL (1, 0) pair is used

PERFORMANCE COMPARISON ON MEDICAL IMAGE SEGMENTATION ALGORITHMS

Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. In this paper explaining current segmentation approaches in medical image segmentation and then reviewed with an emphasis on the advantages and disadvantages of these methods and showing the implemented outcomes of the thresholding, clustering

Model-based learning of local image features for unsupervised texture segmentation

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images

Detection of Brain Tumor in MRI Image through Fuzzy-Based Approach

The process of accurate detection of edges of MRI images of a brain is always a challenging but interesting problem. Accurate detection is very important and critical for the generation of correct diagnosis. The major problem that comes across while analyzing MRI images of a brain is inaccurate data. The process of segmentation of brain MRI image involves the problem of searching anatomical regions of interest, which can help radiologists to extract shapes, appearance, and other structural features for diagnosis of diseases or treatment evaluation. The brain image segmentation is composed of many stages. During the last few years, preprocessing algorithms, techniques, and operators have emerged as a powerful tool for efficient extraction of regions of interest, performing basic algebraic operations on images, enhancing specific image features, and reducing data on both resolution and brightness. Edge detection is one of the techniques of image segmentation. Here from image segmentation, tumor is located. Finally, we try to retrieve tumor from MRI image of a brain in the form of edge more accurately and efficiently, by enhancing the performance of diffe rent kinds of edge detectors using fuzzy approach

"IMAGE DIGEST III: A NEAREST NEIGHBOUR DIFFERENTIAL BASED IMAGE DIGEST GENERATION ALGORITHM "

In this paper we present a methodology to generate a digest for an image based on the grayness value differentials that exist between neighboring pixels. Neighboring pixels are those that lie to the immediate left, immediate right, immediate above and immediate below of a given pixel plus the four pixels that lie in between. This algorithm works on the monochrome images of VGA resolution. Color images are converted to their monochrome equivalents. Images of resolution higher than VGA are converted to images of VGA resolution. The given image is divided into equal sized segments or regions. The pixels of the given image are sampled in such a way that each segment contributes equally to the sampled set for the image. This algorithm uses a histogram based statistical approach towards digest generation. Counters are maintained at the segment level, which keep the raw counts of the differentials for the sampled pixels. The counter values are composed to form the digest for the segment. Computing the digest at the segment level helps to preserve the locality information for the image. The digest for the entire image is a composition of the individual digests generated for each segment or region. The method also provides for the calculation of a lite version of the digest that saves digest space by ignoring the region or locality information

Suorakulmaisen pinnan tunnistaminen lisätyn todellisuuden sovelluksessa

Tiivistelmä. Lisättyä todellisuutta (Augmented Reality, AR) on tutkittu jo jonkin aikaa monenlaisten ammattialojen käyttöön, ja älylaitteiden yleistyessä viihteellinenlisätty todellisuus on myös yleistynyt. Lisätyn todellisuuden sovelluksia on laajasti kehitetty sotilas-, teollisuus-, opetus- ja viihdekäyttöön. Jotta lisätyn todellisuuden sovellukset voisivat toimia tehokkaasti, on sovellusten kyettävä ympäristön havainnoinnin lisäksi muodostamaan havainnoistaan sovelluksen kannalta tarkoituksenmukaisia tulkintoja. Suurimpia haasteita lisätyn todellisuuden sovelluksien kehityksessä on siis sovelluksen saamien havaintojen nopea ja luotettava prosessointi. Tutkimuksen yhteydessä kehitettiin peli, jossa virtuaalinen pallo lisätään pelilaitteen näytölle, ja pelaajan tehtävänä on liikuttaa kättään kameran edessä ja koskea palloa saadakseen pisteitä. Tutkimuksessa hyödynnettiin minitietokone Raspberry Pi2:ta, johon liitettiin kosketusnäyttö sekä kamera. Tutkimuksessa keskityttiin suorakulmaisen pelialueen tunnistamiseen erilaisissa olosuhteissa, kuten huono valaistus tai kun osan pelialueesta peittää jokin tunnistamista häiritsevä este. Pelialueen tunnistamisprosessissa voitiin valita kahdesta erilaisesta segmentointitavasta, Cannyn reunantunnistusalgoritmia hyödyntävästä segmentoinnista tai Otsun metodista. Ohjelman käyttämään suodatukseen pystyi myös valitsemaan monta erilaista suodatustapaa, kuten mediaanisuodatuksen ja Gaussin suodatuksen. Testeistä saatiin monia suuntaa antavia tuloksia. Cannyn reunantunnistusalgoritmin hyödyntäminen auttoi tunnistamaan pelialueen paremmin, kun osa alueesta oli peitetty ja kun pelialue oli kallistunut. Hyvissä olosuhteissa molemmat tunnistusmenetelmät toimivat yhtä hyvin. Otsun metodi auttoi paremmin tunnistuksessa huonossa valaistuksessa sekä oli hieman nopeampi Cannya hyödyntäneeseen metodiin verrattuna.Detection of rectangular surface in augmented reality application. Abstract. Augmented Reality (AR) has been researched for years to be used among several professional areas and since smart devices are becoming more and more common, entertainment use in augmented reality has also become more common. Augmented reality has been applied to military, education, manufacturing and entertainment use. So that augmented reality software can work efficiently, the software needs to be able to detect the environment and make appropriate interpretations. One of the biggest challenges in augmented reality software development is to quickly and reliably process observations. With the research, a game was developed where a virtual ball is added to the devices screen. The players task is to touch the ball while their hand is in front of the camera to score points. The research utilized a minicomputer, Raspberry Pi2, with a touchscreen and a camera attached. The research focused on finding a contour rectangular area in various kinds of circumstances, for example dim lighting or when an object is preventing accurate detection. The detection process for identifying the contour area has two different choices of segmentation methods, a segmentation method that utilizes Canny edge detection algorithm or Otsu thresholding. For filtering, one could choose from multiple filtering methods, for example median filtering or Gauss filtering. The tests gave several approximate results. Canny edge detection utilizing segmentation assisted the detection process better when some of the area was obstructed and when the area was tilted. Both, Otsu segmentation and the Canny edge detection utilizing segmentation assisted the algorithm equally well in good circumstances. Otsu method assisted the detection process better in dim lighting and was slightly faster in calculations