The document outlines the key steps involved in image recognition: 1. Capturing an image digitally and representing it as pixels. 2. Conditioning the image by suppressing unwanted variations to highlight important patterns. 3. Labeling important patterns by detecting edges and applying thresholding to identify significant edges. 4. Grouping edges into lines and objects and extracting features like centroids to represent the image logically. 5. Matching objects in the image to stored models to recognize and identify the objects.

1. IMAGE RECOGNITION STEPS
NIMISHA.T
13MCA11030

2. IMAGE RECOGNITION STEPS

3. FORMATTING
 Capturing an image from a camera and bringing it
into a digital form.
 Digital representation of an image in the form of
pixels

4. OBSERVED IMAGE

5. CONDITIONING
 In an image, there are features which are uninteresting,
either because they were introduced into the image during
the digitization process as noise, or because they form part
of a background.
 An observed image is composed of informative patterns
modified by uninteresting random variations.
 Conditioning suppresses, or normalizes, the uninteresting
variations in the image, effectively highlighting the
interesting parts of the image.
 Applied uniformly and in context-independent.

6. LABELING
 Informative patterns in an image have structure.
 Patterns are usually composed of adjacent pixels which
share some property such that it can be inferred that they
are part of the same structure (e.g., an edge).
 Edge detection techniques focus on identifying continuous
adjacent pixels which differ greatly in intensity or color,
because these are likely to mark boundaries, between
objects, or an object and the background, and hence form
an edge.

7. LABELING (CONTINUE…)
 An edge is said to occur at a point in the image if some
image attribute changes in value discontinuously at that
point. Examples are intensity edges. An ideal edge, in
one dimension, may be viewed as a step change in
intensity;

8. LABELING (CONTINUE…)
One-dimensional edge

9. LABELING (CONTINUE…)
Edge detected image:

10. LABELING (CONTINUE…)
 If the step is detected, the high-valued and low-valued
pixels are labeled as part of an edge.
 After the edge detection process is complete, many edge
will have been identified. However, not all of the edges
are significant.
 Thesholding filters out insignificant edges. The
remaining edges are labeled. More complex labeling
operations may involve identifying and labeling shape
primitives and corner finding

11. LABELING (CONTINUE…)
Thresholding the image:

12. GROUPING
 Grouping can turn edges into lines by determining that
different edges belong to the same spatial event.
 A grouping operation, where edges are grouped into
lines, is called line-fitting.
 The first 3 operations represent the image as a digital
image data structure (pixel information), however, from
the grouping operation the data structure needs also to
record the spatial events to which each pixel belongs.
 This information is stored in a logical data structure.

13. GROUPING (CONTINUE…)
Line-fitting of the image:

14. EXTRACTING
 Grouping only records the spatial event(s) to which
pixels belong. Feature extraction involves generating a
list of properties for each set of pixels in a spatial event.
 These may include a set's centroid, area, etc.
Additionally properties depend on whether the group is
considered a region or an arc. If it is a region, then the
number of holes might be useful. In the case of an arc,
the average curvature of the arc might be useful to know
 Feature extraction can also describe the topographical
relationships between different groups. Do they touch?
Does one occlude another? Where are they in relation to
each other? etc.

15. MATCHING
 Finally, once the pixels in the image have been grouped
into objects and the relationship between the different
objects has been determined, the final step is to
recognize the objects in the image.
 Once an object or set of object parts has been
recognized, measurements (such as the distance between
two parts, the angle between two lines or the area of an
object part) can be made.
 Matching involves comparing each object in the image
with previously stored models and determining the best
match template matching.

16. Example:

17. IMAGE TRANSMISSION
 Transmission of digital images through computer networks
 There are several requirements on the networks when
images are transmitted:
1. The network must accommodate bursty data transport
because image transmission is bursty(high-bandwidth
transmission over a short period)
2. Image transmission requires reliable transport
3. Time-dependence is not a dominant characteristic of the
image in contrast to audio/video transmission.

18. IMAGE TRANSMISSION(CONTINUE…)
 Image size depends on the image representation format
used for transmission.
 Formats:
 Raw Digital Image data transmission
 Compressed image data transmission
 Symbolic image data transmission

19. IMAGE TRANSMISSION(CONTINUE…)
 Raw image data transmission
 In this case, the image is generated through a video
digitizer and transmitted in its digital format. The size
can be computed in the following manner:
 size = spatial-resolution x pixel-quantization
 Eg: The transmission of an image with a resolution of 640 x
480 pixels and pixel quantization of 8 bits per pixel requires
transmission of 307,200 bytes through the network.

20. IMAGE TRANSMISSION(CONTINUE…)
 Compressed image data transmission :
 In this case, the image is generated through a video
digitizer and compressed before transmission.
 Methods such as JPEG or MPEG, are used to
downsize the image.
 The reduction of image size depends on the
compression method and compression rate.

21. IMAGE TRANSMISSION(CONTINUE…)
 Symbolic image data transmission :
 The image is represented through symbolic data
representation as image primitives (e.g..2D or 3D
geometric representation), attributes and other control
information.
 This image representation method is used in computer
graphics.
 Image size is equal to the structure size, which carries
the transmitted symbolic information of the image.

22. THANK YOU