What is the temporal complexity of image feature extraction algorithms, including HS, HOG, MSER and SIFT?

Can you help me by writing a time complexity of algorithms for extracting known image features. I am particularly interested in the detection of Harris-Stephens (HS) angles, extremely stable extreme regions (MSER), histogram-oriented gradient (HOG) and scale-invariant function transformation (SIFT). I've tried to find them in books and online, but I have not found them yet.

research – Karp-Rabin – what is the contribution for the temporal complexity of the worst case?

I'm trying to determine Karb-Rabin's entry in the worst case, regardless of the hash function used. However, I see both answers on the Internet:

  • String "AAAAAAAA" and pattern "AAA"
  • String "AAAAAAAB" and pattern "AAB"

Which of these entries would have the worst time in Karp-Rabin? Thank you!

graphs – Defines the temporal complexity of the Kruskal algorithm as a function

I'm trying to define the temporal complexity of the Kruskal algorithm according to:

  • the number of summits V
  • the number of edges E
  • the temporal complexity of the verification, if two edges do not form a cycle Ec (V)
  • the time complexity of connecting two sets of vertices Vc (V)

The edges are not sorted and I know the time complexity of sorting the edges, which is Big O (E * log E).

I do not really know how to solve this problem and I would be grateful for any help. Thank you!

Spells – Temporal and Spatial Sympathy at the Post-Recognition Launch

The Time 2 Postcognition spell allows a mage to see the past. However, there are confounding factors (emphasis mine):

She can revisit the past of her current location or any time of her life.
own past, or that of an object, with perfect clarity. To focus
meaning on something or anywhere else than the current physics of the mage
location, the mage must also use Space 2. Without the use of Space 2,
she can only do it for a specific place where she was or is.
[..]

and start the dice pool is changed by temporal sympathy

Scenario: You find a stolen painting in the collection of a crime lord. You know when it was stolen and you hit the object, you try to see the moment it was stolen from the museum to identify the thief.

  • If you've never been to the museum and you've only heard about flying, you'll need space 2, that's clear. However, if you have already visited once (level sympathy met), is this requirement waived?
  • If you need Space 2, are you penalized for your spatial and temporal sympathy (that of the object, here at the same time Intime, giving you -4)?
  • If you must use yours temporal sympathy for the target location, have access to the real object help you?

graphs – Temporal complexity of finding a fixed-size match in a hypergraph

Set a size $ k correspondent in a hypergraph $ H = (V, E) $ to be a collection of $ k disjoint edges in pairs in $ E $. Is anyone aware of the time complexity (the best known) of research, for $ k, a size $ k correspondent in an arbitrary hypergraph? I can find many references to the graph problem, but not the hypergraphs.

Temporal complexity – Why was it thought that the test of primality was NP?

To check if $ n $ is first, just try to divide $ n $ in number up to $ sqrt {n} $, which means that the complexity would be $ O ( sqrt {n}) $. In my opinion, $ O ( sqrt {n}) <O (n) $ so this simple algorithm is already P. But why did people think that the primality test is NP and were surprised by the AKS primality test?

graphs – What is the temporal complexity of Prims Algo. without heaps (using the priority queue)?

Here is my attempt:

Initialization –

        For the initialization of the distance table - O (V)
Creating a priority queue - O (V) (because all distances except
the starting vertex is Infinity)

Operation –

        Min extract - O (V)
To search all neighbors for all summits - O (E) * time to
update his new position at the PQ. (here is my doubt)

In my manual, the time to update its new position in PQ is considered O (1), why?

For a single update –

        It's time to find the node that needs to be updated because I do not have any
pointer to her, which is O (V).

So the total time complexity should be – $ O (E * V + {V} ^ 2) $ instead of $ O (E + {V} ^ 2) $

proof techniques – How to prove the temporal complexity of this simple problem of probabilistic inference on a Bayesian network?

Maybe a fairly trivial question, but I try to refresh the methods of evidence in CS …

Suppose we have a simple Bayesian network with two rows of nodes: $ x_1, x_2, ldots, x_n $ and $ y_1, y_2, ldots, y_n $. Each node $ x_k $ takes a state of 0 or 1 with equal probability. Each node $ y_k $ takes state 1 with probability $ p_k $ if $ x_k $ is state 1 and the probability $ q_k $ if $ x_k $ is the state 0.

Is an exponential time needed to calculate the probability that all $ y_k $ are 1, and if so, what is the appropriate CS evidence of this?

Temporal Complexity of Sampling in Gibbs Versus Bulk Gibbs Sampling

What is the computational complexity (giving an upper limit) of a complete Gibbs sampling cycle and a complete Gibbs sampling cycle in blocks with $ frac {n} {k} $ blocks of k $ variables each? Suppose that Gibbs' "systematic analysis" is done at each time.

Let $ Phi $ to be a set of factors on $ X = (X_1, ldots, X_n) $. Let
$ P (X) = frac {1} {Z} prod_ { phi in Phi} phi (D_ phi) $,
or $ D_ phi $ is the scope of the factor $ phi $. Leave each random variable $ X_i $ take values ​​in $ {0,1, ldots, c-1 } $. Leave each variable $ X_i $ occur in at most $ b $ factors. Leave cardinality $ D_ phi $ be delimited by $ a $ for any factor $ phi in Phi $.

The systematic analysis samples all random variables from 1 to n in one order.

algorithm – reducing temporal complexity

Here is the link of the problem
and here is my code,

#understand
int main ()
{
int i, j, k, t, n, c = 0;
int num[105]= {0};
num[0]= 0;
scanf ("% d", & t);
for (j = 1; j <= 45360; j ++)
{
c = 0;
for (k = 1; k <= j; k ++)
{
if (j% k == 0)
{
c ++;
}
}
if (num[c]== 0)
{
num[c]= j;
}
}
for (i = 0; i <t; i ++)
{
scanf ("% d", & n);
printf ("% d  n", num[n])
}
returns 0;
}

The code gives me TLE (time limit exceeded). Is there a better algorithm or procedure to solve this problem? Or, is there a theory of numbers that I can apply to solve it? I think my procedure is not correct, although it gives me a TLE.