Category Archives: Computer Vision

Single color detection using OpenCV

Live and Laugh

After a few days of learning OpenCV, I started to write my first OpenCV program, the requirement is to detect single color and find three largest color objects, it’s a very basic program, but you can know a lot about computer vision if you understand the code. OpenCV provide so many functions, you have to know what you want and what the function does.

This program still run on the Raspberry Pi, frame per second is about 8.5, if you want to find more or less than 3 biggest objects, you can change N. Here is code(You also can get it in my Github) sg_color.c:

Here is makefile:

LIBS= `pkg-config --libs opencv`
CFLAGS= `pkg-config --cflags opencv`
objects= sg_color.o

sg_color: $(objects) 
	gcc $(LIBS)$(CFLAGS) -o sg_color $(objects)

.PHONY: clean
clean:
	rm sg_color $(objects)

Here is processed frame and threshold image:
frame

Threshold

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Visual Intelligence : Human vs Machine

Blog

Ever wondered how visually intelligent our brain is ? or How much has machine vision achieved in mimicking human vision till now ? Lets start by observing a picture

Human Vision (1) Human Vision (1)

Each of these children is observing the world surrounding him/her. They can identify shape and color of various patches in the room. They can also classify objects, the actions of teacher and most importantly ,identify the visually related social behavior of objects in the environment.

By the age of two, our visual cortex becomes so well trained that we can understand any scene without rationalizing its pixel space. This becomes clear from the following example :-

Famous Ponzo Optical Illusion Famous Ponzo’s Optical Illusion

Observe how quickly your brain understands the scene , albeit it falters in guessing that the size of three vehicles are equal. On the other hand, machine vision is still in its infancy. There are algorithms that accurately…

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Researchers are using deep learning to predict how we pose. It’s more important than it sounds

Gigaom

A team of New York University researchers that includes Facebook AI Lab Director Yann LeCun recently published a paper explaining how they built a deep learning model capable of predicting the position of human limbs in images. That field of computer vision, called human pose estimation, doesn’t get as much attention as things like facial recognition or object recognition, but it’s actually quite difficult and potentially very important in fields such as human-computer interaction and computer animation.

Computers that can accurately identify the positions of people’s arms, legs, joints and general body alignment could lead to better gesture-based controls for interactive displays, more-accurate markerless (i.e., no sensors stuck to people’s bodies) motion-capture systems, and robots (or other computers) that can infer actions as well as identify objects. Even in situations where it’s difficult or impossible to see or distinguish a part of somebody’s body, or even an entire side, pose-estimation systems should be smart…

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Computer Vision: UAVs and Video Processing

lmahmed22

On Monday, April 29th DC ACM was privileged to host Dr. Larry Davis, Professor at the Institute for Advanced Computer Studies and the Department of Computer Science at the University of Maryland. His presentation, “Computer Vision: UAVs and Video Processing,” chronicled just how far the field has come since it began in the mid 20th century.

Dr. Davis shared that computer vision technology was initially motivated by the problems facing the postal service in sorting mail. Optical character recognition was developed for postal service applications to address problems of data segmentation, representation, character recognition, and matching. Through this technology postal services were able to automatically sort addresses and packages of all kinds. Computer vision technology enabled the recognition of cursive, characters, logos, stamps, text, and different forms of handwriting.

Successes in the field of computer vision were achieved in other industries as well. In medicine, the ability of retina scans…

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“The Three R’s of Computer Vision” by Dr. Jitendra Malik

Radhakrishna Dasari

As a part of University at Buffalo Distinguished speaker series 2013, Dr. Jitendra Malik, Professor at EECS, UC Berkeley was invited today for a talk on computer vision. The talk started citing Computer Vision as a problem of Recognition, Reconstruction and Reorganization (a substitute word for Segmentation for set of 3 R’s!). Prof. Malik emphasized that computer vision should be taught with the paradigm of 3 R’s rather than starting with Image Processing techniques to segmentation and then going to Pattern Recognition. These problems should not be worked in isolation. He predicted that the interactions between them will influence the future of computer vision research. The talk continued with a note on the history of computer vision. He explained that the aim of computer vision research is to match with the performance of human vision, for which research of Fei Fei Li et al was cited as a reference. The…

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S.U.R.F.

opencv4u

Speed Up Robust Feature extraction algorithm. For those who all are hearing this algorithm for the first time please visit wiki and understand a bit about it.

Now, from name you can guess its something about features. Here I am gonna tell you about its implementation.The following is the code in python using Opencv.

############################################################

import cv2

cap = cv2.VideoCapture(0)
ret,img = cap.read()
#im2 = cv2.imread(‘ex1.jpg’)
#im = cv2.cvtColor(im2,cv2.COLOR_BGR2GRAY)
while True:
ret,img = cap.read()
im= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
surfDetector = cv2.FeatureDetector_create(“SURF”)
surf = cv2.DescriptorExtractor_create(“SURF”)
keypoints = surfDetector.detect(im)
(l,d)= surf.compute(im,keypoints)

for kp in l:
x = int(kp.pt[0])
y = int(kp.pt[1])
cv2.circle(im,(x,y),2,(0,0,255))

cv2.imshow(“features”,im)
cv2.waitKey(1)

#############################################################

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