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Image Classification (icast 2006)

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Imran Sarwar Bajwa, M. Abbas Choudhry [2006], "A Study for Prediction of Minerals in Rock Images using Back Propagation Neural Networks", in IEEE 1st International Conference on Advances in Space …

Imran Sarwar Bajwa, M. Abbas Choudhry [2006], "A Study for Prediction of Minerals in Rock Images using Back Propagation Neural Networks", in IEEE 1st International Conference on Advances in Space Technologies (ICAST 2006), Aug 2006, Islamabad, Pakistan. pp:185-189


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  • 1. Prediction of Minerals by Identifying Various Types of Rocks using Neural Networks Imran Sarwar Bajwa1, M. Abbas Choudhary2 1 Faculty of Computer & Emerging Sciences 2Balochistan University of Information Technology and Management Sciences P.O.BOX – 87300, Quetta, Pakistan Ph: 0092-81-9201051 Fax: 0092-81-921064 imransbajwa@yahoo.com, abbas@buitms.edu.pk Abstract Identifying rocks is less critical in some ways than identifying minerals. A dense, gray mineral is eitherThis paper presents a novel approach for the galena or it isnt. On the other hand, sandstone cansegmentation of ground based images of rocks using grade into siltstone, limestone into dolostone, gabbroartificial neural network architecture. Artificial neural into diorite. If a rock is on the borderline between twonetwork as a color classifier allows a vigorous types, its usually not all that critical where you place it.categorization even under various colors saturationvariations, brightness, and non-homogeneous ambient 1.1- Three Great Rock Familiesillumination conditions. The designed system actuallyidentifies the possible minerals by analyzing the surface In Balochistan province most of the area is rocky andcolor of the rocks. The rocks in Balochistan are very these rocks are mostly sheer rocks. These rocks do nothard and defined. Such rocks are typically full of have greenery and these are coloured rocks. Accordingminerals. The rocks in Balochistan are peculiar in their to various definitions of category these rocks can beshape and surface colour. Usually, these colours are divided into 3 various categories.developed due to the reaction of the particles of theminerals with air. The upper layer if dust upon these • Sedimentary Rocksrocks can be really useful in identifying the possible • Ingenous Rocksminerals concealing inside the rocks. The designed • Metmorphic Rocksmechanism outperforms conventional artificial neuralnetworks since it allows the network to learn to solve a- Sedimentary Rocksthe task through a dynamic adaptation of its These rocks have clear stratification and they are veryclassification context. The designed system is trained by soft and can be easily scratched by a knife. Obviouslyproviding it the basic information related to the made of particles cemented together and mostlyphysical features of various mineral and types of rocks. contains fossils.Keywords: Minerals Identification, Colour layers inRocks, Rock categorization, Colour segmentation.1. IntroductionBalochistan is the largest province of Pakistan in termsof area vast over 347,190 Sq. Kilometers. TheBalochistan Plateau extends westward, averaging morethan 1,000 feet in elevation, with many ridges running Figure – 01 Rock composed of generally roundedacross it from northeast to southwest. It is separated pebble-sized clasts cemented together by a finerfrom the Indus Plain by the Sulaiman and Kirthar materialranges. Most of the area of Balochistan is rocky.Generally, a rock is usually composed of 2 or moreminerals in some physical combination, although somerocks are composed of only one mineral. Examples ofrocks are limestone, coal, sandstone, granite, or shale.
  • 2. change rapidly at the boundary (edge) of two regions. Examples of edge detectors are Sobel, Prewitt, and Roberts [4, 6, 8]. For color images the edge detection can be performed on color components separately (such as R, G, and B). These edges are merged to get a final edged image. Jie and Fei [24] proposed an algorithm for natural color image segmentation. In this Figure 02 - Limestone - rock consisting primarily of technique, edges are calculated in terms of high phase calcite congruency in the gray level image. It uses a K- means clustering algorithm to label the long edge lines. Theb- Igneous-Volcanic global color information is used to detect approximately the objects within an image, while the short edges areIgneous rocks can be further divided into two parts as merged based on their positions.igneous-volcanic and igneous. Igneous-volcanic rockscan be defined as they contain numerous bubble-like 1.3 Region growingcavities that may or may not be lined with minerals and These techniques find the homogeneous regions in anthey also has obvious bubbly or frothy texture. These image [5, 8, 21]. Here, we need to assume a set of seedrocks are fine-grained, uniform in texture, and hard. On points initially. The homogeneous regions are formedthe other hand igneous-plutonic rocks are made of by attaching to each seed point those neighbouringdiscrete mineral grains locked together (may be pixels that have correlated properties [12]. This processloosened by weathering) and they contain large crystals is repeated until all the pixels within an image arein a finer-grained mass. classified. However, the obscurity with region based approaches is the selection of initial seed points. Moreover, it is superior to the thresholding method, since it considers the spatial association between the pixels [19]. The images which correspond to the measurements of local homogeneities at different scales are called as ‘J- Figure 03 - An intrusive, coarse-grained igneous rock images’. The system has the ability to segment color composed of primarily of quartz and feldspars. textured images without supervision. First the colors inside the image are quantized to several classes. The pixels are then replaced by their corresponding color class label which forms the class map of the image. A region growing method is then used to segment the image based on multiscale ‘J-images’. 2- Problem Statement The rocks in Balochistan are very hard and defined. Figure 04 - a fine-grained (extrusive) igneous rock Such rocks are typically full of minerals. The rocks in composed primarily of ferromagnesians with up to 50% Balochistan are peculiar in their shape and surface plagioclase feldspars colour. Usually, these colours are developed due to the reaction of the particles of the minerals with air. Thec- Metamorphic Rocks upper layer if dust upon these rocks can be really usefulMetamorphic rocks have a fine texture with an obvious in identifying the possible minerals concealing insidedirectional grain (foliation) and they have obvious the rocks. The designed mechanism outperformsbands, streaks or clumps of different minerals. These conventional artificial neural networks since it allowsrocks are made of mostly of quartz or calcite but is the network to learn to solve the task through a dynamiccoarse-grained and lacks sedimentary features and they adaptation of its classification context.also contain distinctive metamorphic minerals like 3- Segmentation using neural Networksgarnet or kyanite May often have features of originalrock but is re-crystallized or chemically changed. The field of artificial neural networks has become enormously fashionable area of research in recent1.2 Edge Detection years and ANNs have found numerous successfulAn edge detector finds the boundary of an object. These applications in almost every field of science andmethods exploit the fact that the pixel intensity values engineering. ANNs can easily handle complicated
  • 3. problems [17] and can identify and learn correlated network should be sufficient to behave as a shippatterns between sets of input data and corresponding autopilot.target values. After training, these networks can beused to predict the outcome from new input data. 4- Study Area and Data SetNeural Networks mimic the human learning process Balochistan is one of the four provinces of Paksitan. Itand can handle problems involving highly non- is the largest province of Pakistan in terms of area vastlinear and complex data even if the data are imprecise over 347,190 Sq. Kilometers. The Balochistan Plateauand noisy. They are ideally suited for pattern extends westward, averaging more than 1,000 feet inrecognition and do not require a prior fundamental elevation, with many ridges running across it fromunderstanding of the process and phenomenon being northeast to southwest. Quetta (the word derives frommodeled [18]. They are also highly suited for Pushtu word kwatta, fort) no doubt is a natural fort,applications involving parameter varying and/or time surrounded as it is by imposing hills on all sides. Thevarying systems. As mentioned above, ship steering encircling hills have the resounding names of Chiltan,control system is a parameter varying control system, Takatoo, Mordar and Zarghun. Quetta, the capital ofso neural networks have great potential to control Balochistan lies between 300 - 03’ and 300 -27’ N andthis application. 660 - 44’ and 670 - 18’ E. The total geographical area ofThere are three main categories of ANNs: Quetta district is 2653 Km2, has a population of almostfeedforward, feedback and cellular neural networks. 1.5 millions and stands at the gateway to central Asia.It is well established that the feedforward neural Its strategic location has caused rapid populationnetworks are the most suitable networks for control growth. Following are the type of rocks which has beenapplications. In this paper, the Multilayer Perceptron studied during the research.(MLP) networks have been explored for theapplication [19]. A typical MLP network is shown inFig1. It consists of an input layer, one or morehidden layers and an output layer. The number ofhidden layers and the number of neurons in eachlayer is not fixed. Each layer may have a differentnumber of neurons, depending on the application. Figure 05 - The study area was Quetta and its Figure 05: A Multilayer Perception Network surroundings as Pishin, Loralai, etcThe output layer neurons may have the sameactivation function as the hidden neurons. However,many applications use a linear function as theactivation function of the output layer neurons. Inother words, the output of each of these neurons isequal to its net input. A number of researchers [13]have proved mathematically that an MLP networkwith one input layer, one hidden layer and oneoutput layer is sufficient to approximate anycontinuous multivariable function to any desireddegree of accuracy, provided that sufficiently manyhidden layer neurons are available. This suggests Figure 06 – Use images for training Data set of thethat a properly trained single hidden layer MLP designed system etc
  • 4. frame), one hidden layer with 10 neurons, and 7 output classes, corresponding to dark green, light green, yellow, light orange, dark orange, brown, red, blemished and white as a background class. Figure 07 – Use images for training Data set of the designed system etc figure 08 – Overall architecture of the proposed system To train the network we extracted some pixel examples of the typical colors (dark green, light green, red, brown, yellow, light orange, dark orange and white) using some frames with a graphical interface. A frame corresponds to a digital image of rocks in white background. Once trained, the totality of pixels in a frame was presented to the network (pixel by pixel). Figure 08 – Use images for training Data set of the The network returned all image pixels classifieds as one designed system etc of the system typical colors. The network classification5- Used Methodology was stored (pixel by pixel) and we got an output image from the initial frame. As shown in previous worksThree classes have been defined here according to the [14], simple examples of the colors without brightnesstypes of the rocks. These three classes of rocks have or saturation examples are enough to obtaining abeen identified on the basis of the colors. Under a range satisfactory classification performance (about 97% withof proper illumination conditions, the groups of colors low illumination and color saturation variations) with(orange, green, brown, etc.) can be easily separated by low computational cost.edges. The problem of color classification can thus beseen as a problem of determination of optimum edges 6- Conclusioncapable of a suitable partition of an RGB color space.These edges – capable of processing this separation – The use of an artificial neural network as a colorhave some special characteristics: The edges are not classificator allows a robust classification even undernecessarily regular; The edges of each class are not various colours saturation variations, brightness, andnecessarily of same size; The edges must have some non-homogeneous ambient illumination conditions. Thegeneralization level in such a way that pixels with small approach has proved to be robust with respect to colorvariations in color illuminations and saturation are variations and consequently highly applicable to theevolved by the same edge. proposed domain. It also can easily be applied to sorting systems of other fruits, such as apples, papayas,In order to fulfill these requirements, we propose the lemons, etc. Application and test of this methodologyuse of an ANN multilayer perceptron (MLP), trained for sorting other fruits remains as future trends of thisusing the back-propagation algorithm (Simões, 2000). work.The adopted network used is shown in Figure 4. In thisnetwork model, there are 3 input neurons (that receive However, the approach presents several characteristicsthe triple of color representation of each pixel in a that can restrict future domains: all colors of the domain must be previously known; the network must be trained
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